# Single-photon SPAD imagers in biophotonics:
Review and Outlook

Claudio Bruschini  AQUA, EPFL, Neuchâtel, Switzerland  Corresponding author:
<EMAIL_ADDRESS>These authors contributed equally to the work
presented in this article  Harald Homulle  AQUA, TU Delft, Delft, The
Netherlands  These authors contributed equally to the work presented in this
article  Ivan Michel Antolovic  AQUA, EPFL, Neuchâtel, Switzerland  Samuel
Burri  AQUA, EPFL, Neuchâtel, Switzerland  Edoardo Charbon  AQUA, EPFL,
Neuchâtel, Switzerland

###### Abstract

Single-photon avalanche diode (SPAD) arrays are solid-state detectors offering
imaging capabilities at the level of individual photons, with unparalleled
photon counting and time-resolved performance. This fascinating technology has
progressed at very high pace in the past 15 years, since its inception in
standard CMOS technology in 2003. A host of architectures has been explored,
ranging from simpler implementations, based solely on off-chip data
processing, to progressively “smarter” sensors including on-chip, or even
pixel-level, timestamping and processing capabilities. As the technology
matured, a range of biophotonics applications has been explored, including
(endoscopic) FLIM, (multi-beam multiphoton) FLIM-FRET, SPIM-FCS, super-
resolution microscopy, time-resolved Raman, NIROT, and PET. We will review
some representative sensors and their corresponding applications, including
the most relevant challenges faced by chip designers and end-users. Finally,
we will provide an outlook on the future of this fascinating technology.

11footnotetext: Article submitted to Light: Science and Applications

##  1 Introduction

Individual single-photon avalanche diodes (SPADs) have long been the detector
of choice when deep sub-nanosecond timing performance was required, due to
their excellent single-photon detection and timestamping capability [1, 2, 3,
4]. What did, however, really trigger the exploration and design of large
digital SPAD imagers, potentially manufactured in volume at affordable prices,
was the breakthrough implementation of the first SPADs in standard CMOS
technology [5] (2003). This was soon followed by the first integrated SPAD
array [6] and a host of architectures, ranging from simpler implementations of
the early days, based solely on off-chip data processing, to progressively
“smarter” sensors including on-chip, or even pixel-level, timestamping and
processing capabilities. Modular setups have been designed as well, either
through the combination of SPAD arrays with FPGAs (“reconfigurable pixels”),
or by means of very recent 3D developments. Furthermore, basically all
implementations rely on FPGA-based host boards; this, combined with the
natively digital SPAD data output, opens the door to real-time algorithmic
implementations in close sensor proximity, such as FPGA-based autocorrelation
and lifetime calculations.

As the SPAD technology matured, a range of applications have been explored in
very diverse fields, such as consumer and robotics imaging, data and telecom
security, advanced driver-assistance systems, and biophotonics, which is the
main subject of this review. We will discuss in particular (endoscopic) FLIM,
(multi-beam multiphoton) FLIM-FRET, SPIM-FCS, localization- and entangled
photons-based super-resolution microscopy, time-resolved Raman, NIROT, and
PET. It is, however, true that SPAD imagers are still mostly used in
specialized research settings, apart from some notable non-imaging exceptions,
such as SPAD arrays in the form of silicon photo-multipliers (SiPMs), which
are readily available from a number of manufacturers. This is at first look
surprising, given the aforementioned potential for unrivalled photon counting
and time-resolved performance, but can be partly traced back to some
performance parameters which still lag behind those of established CCDs and
sCMOS imagers, such as quantum efficiency over the whole spectrum and fill
factor, which are of importance for several light-starved applications. The
pixel sizes are typically larger, limiting so far the manufacturing of
megapixel arrays. On the technology side, the design of high performance, low
noise SPADs is challenging; the same is true at system level for data
handling, leading to important firmware development efforts. It therefore
comes to no surprise that recent efforts have been focused, at the device
level, on increasing the SPADs’ key figures of merit [7] and improving the
contact with foundries, to fully profit from possible process optimizations.

In the following sections, which are mostly dedicated to SPAD imagers in
standard CMOS technologies, we will first dwell on the SPAD state-of-the-art,
starting from individual devices, their key properties and the corresponding
optimization trade-offs, which are strongly influenced by the target
application. We will then focus on the impact of design choices on the overall
sensor architecture and the most important challenges, moving up in a vertical
fashion from the pixel level, considering basic circuitries and in-pixel
options, to array architectures (1D vs. 2D), and the read-out, which is of
particular importance for real-time implementations. A host of biophotonics
applications will then be described, starting from FLIM and its different
flavours, to end with more disruptive scenarios and sensor concepts such as
quantum-based super-resolution microscopy and 3D stacking (the combination of
a top sensor layer with a bottom control and processing layer), respectively.

The interested reader is encouraged to look at [3] for details of other
applications of SPAD-based imagers, and [8, 9, 10] for a comparison to
established devices as well as alternative CMOS imagers.

##  2 SPAD detectors and imagers

###  2.1 Single-photon avalanche diodes

Figure 1: (a) Artist’s impression of a SPAD array (top view) and (b) an
example of the corresponding cross section for a substrate isolated SPAD in a
conventional CMOS process, depicting some of the key components (diode
anode/cathode and corresponding p-n junction, multiplication region in which
the avalanche is triggered, substrate and isolation from it) [3]. The SPAD
fill factor can be enhanced with microlenses (c), with the inset showing a SEM
image from [11]. The design of individual pixels ranges from (d) basic
structures, only capable of generating digital pulses corresponding to
individual photon arrivals on the SPAD, to (e) pixels including counters,
which add the individual arrivals over a given time window, possibly gated, or
(f) more advanced electronics such as a complete TDC, which make it possible
to timestamp individual photon arrival times. Corresponding examples of pixel
micrographs are displayed in (g–i), reprinted from [12, 13, 14].

Photodectors capable of measuring single photons have been known for decades
and have been realized using different technologies, from photo-multiplier
tubes (PMTs) to micro-channel plates (MCPs) and electron-multiplying CCDs
(EMCCDs). However, the implementation of large, all solid-state single-photon
imagers (Figure 1(a)) calls for a new kind of miniaturized, scalable device
featuring a reliable performance parameter set. One example of such a device
is represented by the single-photon avalanche diode (SPAD) implemented in
industry-standard CMOS technology. The SPAD is basically a photo-diode whose
p-n junction (Figure 1(b)) is reverse biased above its breakdown voltage
$V_{bd}$, such that a single photon hitting the active (i.e. photosensitive)
device area can create an electron-hole pair and thus trigger an avalanche of
secondary carriers. The avalanche build-up time is typically on the order of
picoseconds, so that the associated change in voltage can be used to precisely
measure the time of the photon arrival [15, 1]. This regime of operation is
known as Geiger mode, and hence the devices are also known as Geiger-mode APDs
(GmAPDs).

The self-sustaining avalanche in the SPAD needs to be stopped as soon as
possible to prevent the destruction of the device itself due to high current.
The corresponding quenching takes place by lowering the SPAD bias voltage
V${}_{\text{OP}}$ below breakdown, e.g. by using a resistor in series with the
SPAD. The voltage across the SPAD then needs to be restored to its initial
value above breakdown, before the following photon can trigger another
avalanche. During this interval, which is typically on the order of tens of
nanoseconds, the SPAD is unable to detect additional photons. This interval is
known as dead time. The change in voltage across the SPAD during a detection
is easily transformed into a digital signal by using a discriminator, for
example a single transistor or an inverter. The resulting output, which does
not depend on the wavelength of the impinging photon, is compatible with
standard electronics, which makes it easy to integrate a SPAD into larger
circuits and arrays of detectors. Table 1 summarizes the most important
properties of SPADs and compares them across the SPAD-based imagers reported
in LABEL:tab:summary_table).

Table 1:  Key SPAD pixel parameters and typical values commonly found in the sensors listed in LABEL:tab:summary_table.  | Value range
---|---
| Dead time | [ns] | 10–100
| DCR | [cps/$\mu$m2] | 0.6–100
| PDP (peak) | [%] | 10–50
| Fill factor | [%] | 1–60
| Timing resolution | [ps] | 30–100
SPAD pixel | Afterpulsing probability | [%] | 0.1–10

A number of parameters are used to describe the performance of a single SPAD
device [7]. The most important is the photon detection probability (PDP),
representing the avalanche probability of the device in response to a photon
absorption at a given wavelength. In CMOS SPADs, the PDP has a peak in the
visible, which can reach 70% for single, optimized diodes. Other important
parameters are dark count rate (DCR), i.e. the observed avalanche rate in the
absence of light, and afterpulsing, which introduces false events that are
correlated in time to previous detections. When SPADs are grouped in imagers
one has to consider electrical and optical crosstalk, as well as fill factor,
which denotes the ratio between the photosensitive area and the total pixel
area; the latter does obviously affect the overall imager sensitivity, being
multiplied by the PDP to give the overall photon detection efficiency (PDE).

Many of the SPAD characteristics can be optimized in the design phase, often
requiring trade-offs. For example, a larger size of the guard ring, to better
contain the high electric field and prevent premature edge breakdown, will
positively impact the crosstalk, at the expense of active area and thus fill
factor. This can be compensated with larger diodes at the cost of DCR, which
increases with diode area. A short dead time allows a higher count rate, and
thus a high dynamic range, but increases the afterpulsing probability, which
leads to problems when detecting photon correlations. The targeted sensor
application should ideally be taken into account during the design phase in
order to select the optimal trade-offs, such as sensitivity vs. noise and
speed vs. fill factor.

###  2.2 From individual SPADs to arrays

When a suitable SPAD device and pixel circuit have been demonstrated in a
given fabrication process, they can be integrated into an array to form a SPAD
imager. The simplest array is linear, allowing the designer to place the
detection and processing electronics outside the photosensitive area, thus
reaching higher fill factors. A 2D array of pixels, on the other hand, needs
self-contained circuits, in-pixel or at the periphery, to acquire, store and
transmit data. This additional circuitry negatively impacts the fill factor,
but eliminates the need for scanning to create a complete image. Some freedom
also exists at the level of the spatial granularity; grouping pixels, for
example, reduces the overall data throughput, while preserving key
information, such as photon timing, and reducing complexity. The same is true
for the temporal granularity, allowing, for example, to acquire only a subset
of all possible timestamps for specific applications. Finally, the sensor
fabrication itself might include post-processing steps, such as the deposition
of microlenses to increase the overall sensitivity (Figure 1(c)).

We will discuss the various architectural choices and the corresponding trade-
offs below, moving from the pixel level up to the array design specificities.

##  3 Architectures

###  3.1 Pixel architecture

We divide SPAD pixel circuits into three broad types, depending on the
functionality added on top of the basic photon-to-electrical pulse conversion.
The first type is represented by a basic structure, which includes only the
circuitry necessary for a full detection cycle consisting of avalanche
generation, quench and recharge. The output of such a pixel is a train of
electrical pulses corresponding to individual photon detections. The second
type is a pixel with built-in counter, consisting of a counting circuit and at
least one bit of memory; its output is a photon count. The third type of pixel
is time-correlated and includes circuitry to discriminate the arrival time of
photons; its output can be as simple as a flag for a detection during a given
time window, or as complex as a variable number of timestamps reporting
distinct photon arrival times. Concept schematics for the three types of
pixels are shown in Figure 1(d–f), while selected implementation examples are
displayed in Figure 1(g–i). The pixel fill factor obtained when assembling an
array is inversely proportional to the amount of electronics placed besides
the SPAD, which makes it advantageous to use modern fabrication technologies
that enable smaller feature sizes.

Pixel design elements common to all types of pixels include active quenching
and recharge, masking and gating. Active quenching and recharge can be
employed to optimize the detection cycle of a SPAD by reducing the dead time
in a well controlled way and thus improving the maximum count rate. In this
case, active circuitry can be used to stop the avalanche and recharge it
earlier than possible with passive resistive approaches. This limits the
amount of charges flowing through the diode, improving its lifetime and
reducing afterpulsing. Active techniques for quenching and recharge have been
employed in a number of designs [15, 16, 17, 18, 12, 19, 20, 21, 11, 22, 23,
24, 25].

Masking is used to selectively disable pixels inside an array. This feature is
commonly employed in designs where multiple pixels share circuitry, in order
to avoid overloading by particularly noisy pixels, which would otherwise
decrease the overall performance. Possible implementations can either switch
off the SPAD, thereby preventing avalanches from taking place, or disconnect
pixels from the read-out circuit. Switching off the SPAD has the additional
benefit of removing possible crosstalk. Examples of pixel architectures with
masking are [19, 21, 22, 23].

Gating is another independent element of pixel architectures and consists in
enabling the SPAD only for a limited time, down to picosecond ranges. The key
advantage of gating is the temporal discrimination that can be achieved
through it, even though the overall detection efficiency is reduced. In a
setup with repetitive (pulsed) illumination, gating permits to selectively
capture photons during a portion of the repetition period, with the added
option of shifting the gating window in picosecond steps. This feature can
also be used to exclude parts of the photon response that are not of interest.
Exemplary gating applications include rapid lifetime determination in FLIM,
the accurate reconstruction of a particular optical response in the time
domain, the elimination of early/background-related detections, or the
reduction of a sample’s intrinsic fluorescence in Raman spectroscopy. Gating
can also be used for the reduction of DCR by eliminating dark counts occurring
outside the time zone of interest.

Moving beyond basic structures, pixels have been designed to include memory
elements for multiple purposes. A single-bit memory can be used to capture a
purely binary image during a given time interval (i.e. frame time), for
example when it is important to avoid global shutter artifacts; this kind of
architecture can be implemented with only a few transistors, thus still
allowing to reach reasonable native fill factors (5-10%), while a fast read-
out (10-100 kpfs) is usually employed in order to increase the dynamic range
and accumulate multi-bit images off-chip [19, 11, 26]. Fabricated in a 130 nm
CMOS process, the pixel in [27] reaches a notable fill factor of 61% by using
an all NMOS design and analog storage element, as well as deep N-well sharing
between pairs of SPAD rows, at the price of a reduction in timing accuracy due
to the simplified gating circuit, and a somewhat increased crosstalk between
pixels.

Multi-bit and multiple counters allow to differentiate between the number of
captured photons. When used together with multiple gates, a simple in-pixel
phase detector can be constructed and read-out requirements can be relaxed,
while maintaining a good dynamic range. Integrating more memory in each pixel
drastically reduces the fill factor and makes it advantageous to move to
smaller technology nodes. For example, the 2$\times$ 8-bit counter pixels
detailed in [28], implemented in an older 0.35 $\mu$m technology, result in a
fill factor of 0.8%, while the 10-bit counter pixel reported in [29], designed
in a 0.18 $\mu$m process, results in a fill factor of 14.4%.

Pixels with integrated arrival time measurement, typically implementing time-
to-digital converters (TDCs) or their analog counterparts (time-to-analog
converters or TACs), represent the most powerful, but also the most complex
pixel architecture. The timing circuitry needs in general to be as compact and
low power as possible in order to be integrated in every pixel of an imager,
while still offering the required timing resolution. Arrays with in-pixel TDCs
do usually not exceed fill factors of a few percent [20, 30, 31, 32]. A ring-
oscillator is typically used for timestamping with a resolution in the tens of
picoseconds (fine measurement), whereas the timing range is extended with a
counter as needed (coarse measurement). Analog techniques, such as in-pixel or
column-level TACs or analog memories in the form of capacitors, are making a
comeback because they can be implemented in area-efficient ways, at the
expense of analog-to-digital converters placed at the periphery of the array
or outside of the chip, together with all the difficulties inherent in mixed-
signal design, e.g. non-uniformities and mismatch. Notable examples of sensors
using analog elements are [33, 34, 4, 24], the last of which presents an array
with a fill factor of 26.8%.

###  3.2 Array architecture

Figure 2: Comparison of SPAD array architectures: (a) in linear arrays, the
pixel electronics can be placed outside the pixel area, leading to a fill
factor increase; in 2D arrays, fill factors are generally smaller, because (b)
electronics is needed inside the pixel itself, or at least (c) at the array
periphery, e.g. for column-based TDCs. The related pros and cons are discussed
in detail in the text, whereas corresponding examples of array micrographs can
be found in (d–f), reprinted from [35, 14, 32]. Finally, (g) provides an
overview of the evolution of SPAD imagers over the last 15 years in terms of
total number of pixels (on the vertical axis), the technology node (indicated
at the image top), and some salient architectural characteristics, such as
Random Access or Event Driven (indicated at the image bottom). Only some
representative examples, primarily targeted at biophotonics applications, are
shown here (details are reported in LABEL:tab:summary_table). The diagonal
lines indicate developments along a given technology node (800 nm, 350 nm, 130
nm), which usually started by optimizing the SPADs before designing full
imagers. The latest years have seen a trend towards higher spatial resolutions
and 3D IC solutions.

The simplest form of a SPAD pixel array is a single line. In a linear (1D)
array (Figure 2(a, d)) all pixel electronics is placed outside the sensor
area, with only the diode guard ring separating the active area of different
pixels. Most linear SPAD arrays allow a truly parallel pixel operation, even
if resource sharing is in principle possible in the same way as for 2D arrays.
The 1D architecture allows to reach the highest possible fill factors, at the
expense of the optical or mechanical scanning solutions that are needed to
generate a 2D image, should this be required by the target application.

Two-dimensional SPAD pixel arrays (Figure 2(b, e)) are capable of acquiring 2D
images directly, at the expense of a more complex sensor design for the
interconnection between pixels and read-out electronics. In general, all
supply, control and data signals are shared across the rows and columns of a
2D pixel array to maximize fill factor. The minimal circuitry needed at the
pixel level is a read-out line driver, but usually more is added, such as
gating and counters with memory, as discussed in the previous subsection. More
complex pixels include timestamping electronics and possibly in-pixel photon
information counting or timing pre-processing. Depending on the application
requirements, some circuit elements, such as complex time-to-digital
converters, can be shared among multiple pixels, either for a larger block of
pixels or, more commonly, for (multiple) rows or columns (Figure 2(c, f)).
Non-uniformities and timing skews grow in general with increasing array size,
and similarly for the overall generated data volume, calling for specific
read-out solutions as discussed below. One possibility is to bin the pixels in
groups, e.g. in situations where the spatial resolution can be traded off vs.
the signal-to-noise ratio (SNR).

Despite the efforts to maximize fill factors in 2D arrays, the obtained values
are usually below those of similarly sized sCMOS cameras (see also
LABEL:tab:summary_table), especially for complex pixel architectures like in-
pixel TDCs, due to the larger transistor counts. Microlenses do therefore
represent a viable option to reclaim some of the fill factor lost to the
electronics. These micro-optical devices are placed in front of the sensitive
area, typically on the surface of the detector, and concentrate impinging
photons onto the active (i.e. photosensitive) pixel area (Figure 1(c)).
Examples of SPAD-related microlens developments and sensors are presented in
[36, 37, 19, 38, 39, 40, 41, 42]. The microlenses are typically optimized for
specific applications and properties (for example collimation) of the
impinging light.

In 2D imagers it is possible that the pixels are no longer strictly operated
in parallel, for example when they contain memory elements which are addressed
and then reset by the read-out (to gather new photons) on a row-by-row basis
(rolling shutter acquisition operation). This can lead to well-known
artifacts, such as temporal lag, when recording phenomena with very fast
dynamics. Some imagers do therefore foresee true global shutter operation,
which provides an image snapshot at a given instant. This can be achieved by
activating all pixels together at the start of a frame, to then “freeze” data
acquisition at the end of the frame and start the read-out operation, with
some loss in efficiency (reduced temporal aperture). An alternative which does
not call for (many) expensive global signals is represented by an event-driven
operation mode, which allows continuous on the fly recording of events as they
occur; one way of implementing this is by using a common bus shared by all
pixels (e.g. in a column), together with separate address lines to identify
the SPAD that has fired.

Considering all trade-offs, encountered when selecting a pixel and array
architecture, there is no single best implementation. The SPAD array’s
architecture should therefore be chosen based on the target application,
sometimes even foregoing classical imaging approaches, or at least profiting
from the flexibility that SPAD arrays provide, e.g. by binning pixels and pre-
processing data close to the sensor. As an example, we consider time-of-flight
positron emission tomography (ToF-PET), where the information of interest is
represented by the energy, time-of-arrival and interaction coordinates of
gamma rays; the latter are converted by means of scintillating crystals into
visible light photons, to be detected by SPAD arrays in the form of SiPMs. In
this case, it makes sense to reduce the effective granularity of the recorded
data by grouping multiple SPADs together and compensate for noisy detectors
using masking. The gamma ray energy is given, in this digital approach, by the
total number of SPADs that have fired in a time window of a few hundred
nanoseconds, while the time-of-arrival can be estimated on chip and refined on
the local control and communication FPGA. An overview of digital approaches to
SPAD-based sensors for PET is provided in [43], while individual detector
architectures are detailed in [21, 44, 45, 22].

###  3.3 Read-out architecture

One of the main concerns when interfacing with a single-photon camera is the
resulting high data rate, especially when recording timestamps of individual
photons or working at very high frame rates. Eventually the data rate needs to
be reduced to a level where it can be transferred to a computer or storage
medium. This can be realized, for example, with the same approach as in a
streak camera, whereby information captured during a (very) short duration is
stored locally (in the pixel) at high speed, and then read out at low speed
for processing and storage. Another possibility is represented by _in situ_
extraction of higher level information. The corresponding algorithms, like
histogram accumulation or multi-bit count integration, can be implemented on
the control FPGA, or even on the sensor itself. In the case of fluorescence
lifetime imaging, for example, real-time systems have been devised that are
capable of calculating the lifetime of molecules at video rate, without the
need for recording the full single-photon data stream [46, 47, 9], including
for multi-exponential scenarios [48]. An FPGA system indeed offers some
flexibility in terms of possible data processing and high computational
bandwidth, which can be used for example to realize firmware-based
32$\times$32 autocorrelator arrays as detailed in [49] (with FCS as target
application). The “reconfigurable pixel” concept proposed in [50, 35]
maximizes flexibility by moving the whole circuitry, which is usually placed
beyond the basic SPAD pixel structure, inside the FPGA; this makes it possible
to implement different TDC or counter architectures, with the goal of
tailoring the system (sensor plus firmware) in an optimal way to the target
application’s needs.

###  3.4 SPAD sensor summary

LABEL:tab:summary_table lists a comprehensive summary of the main SPAD-based
sensors and imagers which have been targeted at biophotonics applications;
they are discussed in the next section, together with the related applications
and the corresponding results. A representative subset is shown in Figure
2(g), which provides a graphical overview of how these imagers evolved over
the last 15 years.

##  4 Biophotonics applications

The following sections analyze in detail a host of biophotonics applications
that have been explored with SPAD imagers, starting from FLIM, which has been
addressed early on, and its different flavors, to end with more disruptive
scenarios and forward looking sensor concepts.

##  5 Fluorescence lifetime imaging

Fluorescence lifetime imaging (FLIM) offers a non-invasive measurement
technique for applications where the fluorescence intensity does not provide
sufficient information or discrimination. Common usage of FLIM is found in the
study of living tissues and cells at the molecular level, due to the fact that
fluorescence lifetime is insensitive to fluorescence intensity and to the
corresponding probe concentration, at least to a reasonable extent [51]; the
samples should, however, not be subject to excessive illumination intensities,
to avoid phototoxicity and photobleaching. Other advantages are the detection
of lifetimes which can be dependent on pH, temperature, oxygen concentration
and viscosity levels, thereby enabling the detection of effects that cannot be
observed with simple fluorescence intensity measurements.

Slow acquisition speed is the main limitation of standard FLIM setups. While
the photo-physics at the molecular level contributes to this, the detection
system can impose major speed limitations as well. Time-correlated single-
photon counting (TCSPC), which requires the timestamps of individual photons,
is often the detection method of choice due to its very high precision, but
the underlying hardware and data acquisition are hard to scale to large
multichannel arrays; scanning is therefore required when used in an imaging
setup. Time-gated sensors have also been employed, including large sensitive
areas; they rely on one or more (moving) gates to recover the timing
information, and thus the lifetime, at the price of a reduction in the overall
sensitivity, as discussed in the Architectures section.

The interested reader is referred to [52, 53, 9, 10, 54] for background
literature on FLIM and related sensors, techniques and applications. We will
focus in the following subsections on standard CMOS SPAD implementations for
FLIM, and how these have been engineered and employed to address the
aforementioned limitations.

###  5.1 Point-like FLIM

Point-like FLIM systems offer increased signal-to-noise ratios by combining
the individual outputs of several pixels. An example of such an approach is
represented by the FluoCam [25] system, which comprises a 60$\times$48 SPAD
array [28], with two 8-bit time-gated counters in each pixel. The gates can be
externally programmed to shift in steps of roughly 12 ps, so as to cover a
full laser repetition period with high accuracy. The two counters do therefore
allow a precise reconstruction of the fluorescence response, even when
significant photobleaching distorts the signal, including for sub-nanosecond
lifetimes. The integration times are of the order of several minutes, but can
be substantially reduced by resorting to more recent designs and/or technology
nodes.

The FluoCam system has been used in several _in vivo_ studies to demonstrate
the capabilities of such an approach, employing indocyanine green
(ICG)-modified derivates, such as ICG-RGD, which target the
$\alpha_{v}\beta_{3}$ integrin; the final goal was to explore the feasibility
of surgical applications with exogenous NIR targeted fluorophores [55, 25].
The system was capable of discriminating between healthy (muscle and tail) and
cancerous tissues in a mouse with a glioblastoma mouse model, even though the
lifetime difference was only about 50 ps (10% level in this case) between the
lifetimes of the bound and unbound fluorophores as shown in Figure 3(a).

Figure 3: Examples of fluorescence intensity and/or lifetime results: (a)
FluoCam system used in a point-like mode - study of monomeric ICG-_c(RGDfK)_
injected in a mouse with a glioblastoma mouse model. A subtle lifetime shift
between tumor and non-tumor tissue is observed [25]. (b) Dual-color intensity
fluorescence image of a thin slice of a plant root stained with a mixture of
Safranin and Fast Green, taken with the SwissSPAD widefield time-domain gated
array [56]. (c) Triple-color intensity fluorescence image of HeLa cells
labeled with DAPI, Alexa 488, and Alexa 555, taken with SwissSPAD2 [57]. (d,
e) Label-free FLIM of an unstained liver tissue excised from a tumorogenic
murine model [58], imaged with a 64$\times$4 SPAD array [17]. (f, g) A
Convallaria FLIM measurement done with a linear 32$\times$1 SPAD array [59].
Images reprinted from [25, 58, 56, 57, 59].

In TCSPC mode, the detection system’s limitations introduce pile-up effects at
detection rates $>$0.1 photon/laser cycle, causing underestimation of the
lifetime. Pile-up correction using standard SPAD detectors has been discussed
in detail by Léonard _et al._ [60], who show that low count rates are not
necessarily needed to avoid pile-up, at the price of an increase in the
lifetime estimation variation. In integrated SPAD detectors, multiple
subsystems contribute to the pile-up. A detailed analysis of the SPAD, timing
and routing dead time influence on lifetime estimation can be found in [61],
with experimental results based on a mini-silicon photomultiplier
(32$\times$32 SPAD pixels) implemented in a 130 nm CMOS process, and featuring
an on-chip lifetime estimator [62, 63]. The outputs of all pixels are routed
towards 16 TDCs that can timestamp up to 8 photons per excitation period.
These timestamps are then processed in a center-of-mass module to derive the
fluorescence lifetime. This technique has been validated with reference
samples with relatively long lifetimes of over 1 ns, demonstrating that
reliable lifetimes can be estimated, with a proper architecture, at photon
count rates that go well beyond the classical pile-up limit.

###  5.2 Linear SPAD arrays and corresponding FLIM applications

The pros and cons of linear architectures have already been discussed at the
beginning of the Array architecture section. Pancheri _et al._ implemented a
64$\times$4 linear SPAD array (overall size of 1660$\times$104 $\mu$m2 in a
0.35 $\mu$m CMOS technology) targeted for FLIM [17]. The four SPADs in each
column were connected to the same read-out channel, creating macro-pixels to
reduce the influence of the single SPAD dead time ($\sim$50 ns). This
increased the photon throughput of a 15.8$\times$63.2 $\mu$m2 macro-pixel. The
chip also featured four time-domain gates that were connected to four
separated counters, enabling to construct on-chip histograms of photon-arrival
times with four bins, and to compress the data. This sensor was later used
[58] for spectrally resolved FLIM (sFLIM or $\lambda$FLIM), a setup which
enables to separate molecules by both the fluorescence emission wavelength and
the fluorescence lifetime [64]. An example of a corresponding tissue image is
shown in Figure 3(d, e). The $\lambda$FLIM system eases the discrimination of
different fluorophores and enables to study donor and acceptor molecules
simultaneously.

More recently, Krstajić _et al._ presented a linear 256$\times$2 SPAD array
with pixel pitch of 23.7 $\mu$m and a high fill factor of 43.7%, implemented
in a 130 nm CMOS process [23, 65, 66]. Each pixel was connected to one TDC
with a 40 ps LSB. The sensor also featured an on-chip center-of-mass (CMM)
calculation for mono-exponential fluorescence lifetimes, enabling to output
lifetimes at a 200 Hz line rate with up to 65 kphotons/pixel (limited by the
SPAD dead time). Alternatively, the chip can output per-pixel TCSPC histograms
with 320 ps bin resolution. Multicolor microspheres and skin autofluorescence
lifetimes were measured, with data acquisition of 5 minutes for TCSPC data, to
be compared with 2/200 ms when operating in the CMM mode for fluorophores in
cuvette/skin autofluorescence, respectively. This chip is currently being used
in the EPSRC UK PROTEUS project, targeting _in vivo_ , _in situ_
microendoscopic instrumentation for lung diseases diagnosis. Erdogan _et al._
designed a new generation linear array, in the same 130 nm CMOS process,
extending the resolution to 1024$\times$8 [67]. The chip features 512 TDCs and
on-chip histogramming that decreases the output data rate and mitigates the
I/O and USB bottlenecks.

Linear arrays can also be used as single point detectors, for example by means
of optical 1D to 2D transformations, to reduce the effect of single SPAD dead
time and increase the throughput in FLIM measurements [59]; an example of the
corresponding results is shown in Figure 3(f, g). This and other approaches
could open the way to high-throughput biotechnological applications, such as
high-throughput screening or cell sorting [60, 68], based on nanosecond-lived
fluorophores.

###  5.3 Widefield time-domain gated FLIM

Gated SPAD arrays are in principle easier to implement over large areas than
TCSPC solutions, and thus provide an appealing path to all-solid-state
widefield, time-resolved imaging.

An initial implementation of a time-gated 128$\times$128 SPAD array with 1-bit
memory combined an on-chip 600 ps delay line and an off-chip 200 ps delay line
for gate shifting [19]. DNA molecules labeled with Cy5 were placed directly on
the chip surface and the lifetime was measured. Time-gating enabled excitation
elimination without the need for dichroic mirrors.

SwissSPAD [11] is a 512$\times$128 SPAD array with an in-pixel 1-bit memory
and time-gating capability. The 1-bit frames are read out at 156 kfps, while
time-gating enables independent global exposures as short as 4 ns. The gate
position can be shifted in 20 ps steps with respect to a reference signal.
This enables to reconstruct exponential lifetimes per pixel. The implemented
pixel, which contains 12 transistors, has a 5% fill factor due to the 0.35
$\mu$m manufacturing process. With the use of microlenses, the effective fill
factor is increased to 50–60% for collimated light, as featured often in
microscope output ports [41]; a representative fluorescence intensity image is
shown in Figure 3(b).

Early characterization of SwissSPAD (without microlenses) for FLIM
measurements was performed with reference data sets [69]; the sensor was found
to be able to properly extract the lifetime of fluorophores in the nanosecond
range. Ulku then designed SwissSPAD’s successor, SwissSPAD2 [26, 57], a
512$\times$512 SPAD array - the largest time-resolved SPAD image sensor to
date - with higher PDP and lower DCR, based on a similar architecture. A
triple-color fluorescence intensity image is shown in Figure 3(c). Further
research on widefield time-domain gated FLIM with microlense-enabled versions
of SwissSPAD architectures is ongoing, including real-time phasor-based
measurements [70, 57].

Following up on the proof-of-concept work by Pancheri _et al._ [71, 72, 73],
Perenzoni _et al._ designed a 160$\times$120 SPAD imager with gating, but with
multi-bit memory [34]. The gate can be set as short as 750 ps, with rise and
fall times down to 200 ps, and a frequency of 50 MHz. Instead of a 1-bit
memory, this chip uses an analog counter, enabling multiple photon
accumulations per frame at the cost of introducing ADCs. The pixel pitch is 15
$\mu$m, resulting in a 21% fill factor in a high-voltage 0.35 $\mu$m CMOS
technology. Gyongy _et al._ pursued an n-well shared pixel approach to achieve
a high native fill factor of 61% for a 256$\times$256 SPAD array with 4 ns
gates, 600 ps fall times with on-chip delay generator, and a pixel pitch of 16
$\mu$m [27].

###  5.4 Widefield TCSPC FLIM

A 64$\times$64 40 $\mu$m pitch pixel array designed in 0.35 $\mu$m high-
voltage CMOS technology, featuring 64 column-parallel TDCs and a timing
resolution of around 350 ps, represented early SPAD TCSPC array work [74, 75].
The maximum PDE of 0.1%, though, remained relatively low.

The MEGAFRAME32 high-performance sensor was smaller (32$\times$32 SPADs) but
adopted a radically different architecture, based on 50 ps, 10-bit in-pixel
TDCs, working at a maximum rate of 500 kfps [14, 20], recording either time-
correlated data (one timestamp per pixel), or time-uncorrelated data (6-bit
counting). In the former operation mode, up to 0.5 billion timestamps could be
generated per second [76]. The fill factor (1%) did suffer from the large in-
pixel electronics; on the positive side, this did stimulate pioneering
microlense work to bring the fill factor back up. MEGAFRAME32 was extensively
employed to explore bio-applications and subsequently extended to a
160$\times$128 array (MEGAFRAME128), adding peripheral intelligence (data
compression, CMM pre-processing) [30, 77, 78].

Gersbach _et al._ report on early high frame-rate FLIM proof-of-concept
investigations [79, 20] with MEGAFRAME32. Li _et al._ [46, 47, 80] illustrate
how firmware-based rapid lifetime estimation algorithms, such as CMM (center-
of-mass method), make full use of the large number of available timestamps to
enable video-rate (50 fps) real-time FLIM operation. An example of the
corresponding _in vivo_ two photon FLIM data, both intensity and lifetime, is
shown in [80] using an FITC-albumin probe, which was injected into a rat
bearing a P22 tumor and measured 100 minutes after injection. A clear
distinction between the blood vessels and the tumor tissue could be observed
in the lifetime image (bi-exponential decay), in contrast to the intensity
image. Another widefield FLIM application of the same sensor, coupled to DNA
microarrays, is reported in [81] employing a TIRF excitation geometry.
Distinct lifetime signatures, corresponding to dye-labelled HCV and quantum-
dot-labelled HCMV nucleic acid targets, could be distinguished over 320
pixels, with concentrations as low as 10 nM and an exposure time of 26
seconds.

A different architecture was selected by Field _et al._ for their 64$\times$64
array, in a standard 0.13 $\mu$m CMOS process, reported in [31, 32], namely
one TDC per pixel (LSB of 62.5 ps) but placed at column level; this led to a
pixel pitch of 48 $\mu$m. The sensor was aimed at video rate operation (100
fps), but the corresponding extreme datarate of 42 Gbps entailed a high power
consumption (14.5 mW/pixel).

Parmesan _et al._ [33] have chosen to emphasize small pixel pitches (8
$\mu$m), while still maintaining a pixel fill factor of nearly 20%, by
resorting to an architecture based on in-pixel time-to-amplitude (TAC)
converters, with a global ramp voltage. This enabled the design of large
256$\times$256 array, which can work either interfaced to external TDCs
(optimal timing performance but slower system) or using an on-chip coarse
flash ADC (lower temporal resolution).

###  5.5 Multibeam FLIM

Multibeam architectures enable to increase the photon throughput and reduce
FLIM acquisition time. Coelho _et al._ and Poland _et al._ used MEGAFRAME32
with Spatial Light Modulators (SLM) for multibeam multiphoton FLIM, increasing
the throughput by the number of parallel beamlets [82, 83, 84, 85]. Fill
factor does not decrease the sensitivity in such setups because beams are
concentrated onto the active area of SPADs. FLIM data of live cells (MCF-7
human carcinoma cells) labeled with green fluorescent protein was acquired
within 500 ms, at reduced accuracy. This approach was extended in [86] with a
new CMM method, mostly implemented in hardware, capable of pixel level
background subtraction and not requiring prior knowledge of the expected
lifetime; real-time operation was obtained, at reduced accuracy compared for
example with TCSPC mean squared fitting techniques.

Vitali _et al._ also used a multibeam approach with a 32$\times$32 array
(square pixels of 100 $\mu$m with a 20 $\mu$m circular SPAD and 8-bit
counters) and measured FLIM of eGFP in living HEK293-T cells [87]. The SPAD
sensor was implemented in a standard CMOS process and included time-gating
with minimum gate width of 1.5 ns and delay steps below 100 ps.

##  6 Förster resonance energy transfer (FRET)

Förster (fluorescence) resonance energy transfer (FRET) uses interactions
between two different chromophores (light-sensitive molecules) that non-
radiatively transfer energy from a donor to an acceptor molecule. The energy
is transferred only when the distance between the two molecules is small
enough (nm scale, establishing a long-range dipole-dipole coupling), and when
there is sufficient overlap between the emission spectrum of the donor and the
excitation spectrum of the acceptor. During this coupling, one can observe a
decrease in the donor fluorescence and an increase in the acceptor
fluorescence. A typical application is the study of protein-protein
interactions and the measurement of distances between molecular groups in
protein conformations [88].

FLIM-FRET not only measures the fluorescence intensity change in the donor and
acceptor emissions, but also the shortened lifetime of the donor molecule as a
result of quenching [51]. By measuring the ratio between the quenched and non-
quenched lifetime, the donor-acceptor interactions can be quantified
independently from the molecule concentrations within a diverse sample (in
contrast to emission intensity FRET).

Poland _et al._ implemented a MEGAFRAME32-based multifocal FLIM-FRET detector
system combined with the optical setup mentioned in the Multibeam FLIM section
[82, 83, 84, 85]. While scanning protein-protein interactions in live cells
with a frame time of 500 ms, they studied changes in FRET interactions between
epidermal growth factor receptors (EGFR) and adapter proteins Grb2, as well as
a ligand-dependent association of HER2-HER3 receptor tyrosine kinases. Kufcsák
_et al._ used 5-carboxyfluorescein as a donor and methyl red as an acceptor in
FRET for thrombin detection [66]. Thrombin cleaves the connection between the
donor and acceptor, separating them in space and removing the energy transfer.

##  7 Fluorescence correlation spectroscopy

Fluorescence correlation spectroscopy (FCS) measures fluorescence intensity
fluctuations in time, with the aim of estimating the concentration and
diffusion coefficients of fluorophores, including in live cells. These
parameters are extracted from the autocorrelation of the temporal intensity
fluctuations. Faster sensors and imagers enable to study smaller and faster
molecules [89]. In a widefield setup, the correlation between signals from
distant volumes measures the direction and velocity of the flow between them.

###  7.1 Multibeam FCS

The multibeam parallelisation principle introduced in the Multibeam FLIM
section can also be applied to FCS setups, generating a large number of laser
foci using SLMs or diffractive optical elements (DOEs), while taking care to
minimize the background signal generated by out-of-focus light. A single SPAD
or a group of SPADs are then used to detect fluorescence from each laser
focus. Goesch _et al._ used a small, fully integrated 2$\times$2 CMOS SPAD
array for the pioneering work on multibeam FCS reported in [90]. The multibeam
concept was later extended to 8$\times$8 spots to image bright 100 nm diameter
fluorescent beads in solutions, using a 32$\times$32 SPAD array [91]. The
latter was then employed, together with the 32$\times$32 multibeam setup
previously described in [87], to measure FCS of quantum dot diffusion in
solution [92, 93, 87]. Researchers used a DOE to generate 32$\times$32 spots
with a pitch of 100 $\mu$m and a diameter of 12.5 $\mu$m at the image plane
(to match the sensor dimensions).

Independently, Kloster-Landsberg _et al._ [94] used the 32$\times$32
MEGAFRAME32 sensor to measure multifocal FCS with live cells, employing a
frame time of 2 $\mu$s. In this setup, 3$\times$3 laser foci were used for
experiments with free eGFP in HeLa cells. A larger multibeam array could not
be employed due to high crosstalk between closely packed spots which emerge in
this kind of setups.

###  7.2 Widefield SPIM-FCS

FCS coupled to single plane illumination microscopy (SPIM-FCS) enables faster
characterization of 3D samples, recording intensity fluctuations over a
widefield plane. By illuminating a micrometer-thick light sheet in a
$z$-section, out-of-focus light, photobleaching and photodamage can be
minimized. First SPIM-FCS data obtained with a SPAD array was presented in
[95]. The work compared the RadHard2 32$\times$32 SPAD array [96] with EMCCD
and sCMOS cameras. The RadHard2 camera achieved very high read-out speeds, up
to 300 kfps, enabling it to extract diffusion coefficients down to 3 $\mu$s,
with the best precision compared to EMCCD and sCMOS. The camera was coupled
with a real-time 32$\times$32 autocorrelator, implemented on FPGA [49], which
allowed real-time autocorrelation calculation for small molecules in solution,
down to 10 $\mu$s diffusion coefficients. However, RadHard2 did not feature
microlenses, which obviously affected the overall sensitivity and limited _in
vivo_ applicability.

Widefield _in vivo_ SPIM-FCS with SPAD arrays was firstly demonstrated with a
microlensed version of SwissSPAD [11, 41, 69] by Buchholz _et al._ and Krieger
_et al._ [97, 98, 99]. FCS results in HeLa cells are shown in Figure 4 for
three different oligomers of eGFP. The autocorrelation curves of these
measurements featured afterpulsing-like increased correlations at short time
lags; these artifacts were mitigated by using spatial cross-correlations
[100], which also allowed to determine absolute diffusion coefficients without
prior calibration. Although sensitivity needs to be further increased, this
work showed that SPAD arrays can measure protein diffusion in live cells with
a better SNR than EMCCD cameras and a minimum lag time of $10^{-5}$ s.
Correlation algorithms were also extended to GPUs.

Figure 4: Widefield SPIM-FCS images of monomeric eGFP oligomers in HeLa cells
as recorded with a SwissSPAD widefield imager: (a) fluorescence intensity, (b)
diffusion coefficient, and (c) dye concentration. (d) Diffusion coefficients
for three HeLa cells expressing different oligomers. (e) Particle
concentration for the three HeLa cells with different oligomers. Images
reprinted from [99].

##  8 Single-molecule techniques

Single-molecule fluorescence spectroscopy exploits a low concentration regime
to excite individual molecules in a very small volume, and collect rare,
burst-like fluorescence emission events in correspondence to the transit of
individual molecules (whereas in FCS the concentration is such that $\sim$1 or
more molecules are present in the excitation volume at any time – see also
[101] for a thorough, SPAD-oriented analysis). The number of generated photons
is small (a few dozen or less in a large fraction of bursts), resulting in a
quite challenging detection. This in turn imposes stringent requirements on
the photosensor(s), in terms of sensitivity (including in the red, where
standard CMOS is not advantaged due to the low absorption of silicon), fast
response time, low noise (read-out or DCR for SPADs), and high count rates,
also to be able to separate successive or nearby molecules. Measurement times
are usually long, due to the need of accumulating sufficient statistics.

Several (small) arrays of devices fabricated in custom technologies have been
employed over the years with success [102, 103, 104, 2, 105, 101, 106], and
parallelization strategies have again been adopted to increase the overall
throughput, often employing sophisticated optical setups (multispot excitation
and detection). For example, a parallel 8-spot single-molecule FRET (smFRET)
analysis system is described in [107, 108] using 8-pixel SPAD arrays, as well
as in [109], where the authors tackle realtime kinetic analysis of promoter
escape by bacterial RNA polymerase, confirming results obtained by a more
indirect route.

An extension to 48 excitation spots and two 48-pixel SPAD arrays is detailed
in [110], employing two excitation lasers in order to separate species with
one or two active fluorophores. Apart from successfully tackling the multispot
setup issues, the resulting smFRET capabilities are shown on a set of doubly
labeled double-stranded DNA oligonucleotides with different distances between
donor and acceptor dyes along the DNA duplex. The resulting acquisition times
are drastically reduced to seconds. This could in turn enable high-throughput
screening applications and real-time kinetics studies of enzymatic reactions,
potentially propelling single-molecule analysis from a niche technology to a
mainstream diagnostics and screening tool [109].

##  9 Localization-based super-resolution microscopy

The optical resolution is fundamentally limited by diffraction, whereby Abbe
defined the corresponding limit to be $\frac{\lambda}{2n\sin{\theta}}$, where
$\lambda$ is the emission wavelength, $n$ the refractive index, and $\theta$
is the half-angle of the cone of light which enters into the objective [111].
Several super-resolution techniques have emerged over the years to overcome
it, enabling resolution enhancements from initial values of 200 nm down to 10
nm [112]. One such technique uses sparsely activated (blinking) fluorescent
molecules for single-molecule localization [113], whereby the location of each
molecule is determined by the center of its point-spread function. The final
pointillistic image is then reconstructed by merging thousands of frames with
hundreds of thousands of localizations, with a resolution limited by the
localization precision and labeling density. Due to long frame sequences, tens
of seconds to minutes are necessary to reconstruct the final super-resolved
image; this time could be shortened by resorting to higher frame rates and/or
stronger laser intensities.

Figure 5: SPAD super-resolution images: (a) The first super-resolution image
captured with SwissSPAD, compared to (b) EMCCD and (c) widefield. Images show
microtubuli of an U2OS cell labeled with Alexa Fluor 647, in Vectashield
[114]. (d, e) Comparison of SPCImager using “smart” aggregation and
microlenses with an EMCCD. Images show multiple GATTA-PAINT 40G nanoruler
localizations [42]. (f) Comparison of differences in localization uncertainty
with and without “smart” aggregation and the microlenses impact [115, 42]. (g)
SwissSPAD super-resolution image of microtubuli labeled with Alexa 647 in OxEA
buffer, compared to (h) sCMOS and (i) widefield [114]. The white bar shows 1
$\mu$m. Images reprinted from [114, 115, 42].

Antolovic _et al._[116] demonstrated the first localization-based super-
resolution (SRM) images obtained with SPAD arrays, compared in Figure 5(a) to
EMCCD (Figure 5(b)) and widefield (Figure 5(c)), by employing the SwissSPAD
imager [11]. Microlenses were deposited on the SPAD array to improve the fill
factor from the native 5% to 60% [41], which was a key enabler for
sensitivity-critical applications. The image resolution was analyzed with the
Fourier ring correlation method [117], yielding around 100 nm resolution. The
estimated localization uncertainty [118] was 30 nm, with 200 photons per
localization, compared to 15 nm obtained with an EMCCD camera, using 1800
photons per localization. In other terms, although almost 10$\times$ more
photons were acquired with the EMCCD camera, the localization results were
only 2$\times$ better; the reason is that the SNR, which should indeed
increase by $\sqrt{10}$, is reduced by $\sqrt{2}$ due to the multiplication
noise in the EMCCD camera itself, which arises from the electron
multiplication process. Later results with an improved buffer [119], shown in
Figure 5(g–h), yielded 800 photons per localization with a localization
uncertainty of 10 nm for an sCMOS camera, while SwissSPAD collected 100
photons under the same conditions, leading to an uncertainty of 20 nm [114].

When compared to standard EMCCD and sCMOS cameras, SPAD imagers eliminate
read-out noise by utilizing a direct photon-to-digital conversion – the
digital nature of the SPAD imagers enables fast and noiseless read-out. These
properties of SPAD imagers were used in three ways. First, Gyongy _et al._
[115] used temporal oversampling and “smart” aggregation to determine the
start and end of the fluorophore blinking in a more accurate way, so as to
minimize the background noise. Second, Antolovic _et al._ [114] investigated
the performance of the localization algorithm at different frame rates.
Finally, SPAD imagers were used to perform a widefield analysis of fluorophore
blinking in the $\mu$s range [114].

Concerning the first point, Gyongy _et al._ used the 320$\times$240 SPAD
SPCImager [24] for localization-based super-resolution imaging [115, 42]. By
employing microlenses, they improved the effective fill factor from 27% to 50%
and used this sensitivity enhancement to demonstrate 40 nm resolution. A
GATTA-PAINT 40G nanoruler was used as a reference, with the corresponding
results shown in Figure 5(d–f). “Smart” aggregation decreased the localization
uncertainty by 20%; simulations show a potential improvement by 50%. Although
the SPAD results have a localization uncertainty comparable to EMCCDs, a
3$\times$ lower sensitivity in the green entails a lower number of
localizations.

Concerning the second point, Antolovic quantified the optimum frame rate given
the exponential distribution of fluorophore blinking [114], if oversampling
and smart aggregation cannot be used. Oversampling leads to higher
localization uncertainty, while undersampling causes a drop in the number of
localizations.

Finally, SwissSPAD’s high frame rate of 156 kfps was used to explore
additional high-frequency $\mu$s blinking, which reduces the total number of
collected photons and thus the final resolution. As an example, the percentage
of high-frequency blinking Alexa 647 molecules decreased from >68% to <30%
when switching from a MEA to an OxEA buffer [114]. This indicates that high
speed SPAD imagers could be beneficial for fluorophore/buffer design and
optimization.

As the previous discussion has indicated, the fundamental differences between
EMCCD, sCMOS and SPAD imagers do also need to take into account, in addition
to the overall sensitivity, the different noise contributions and achievable
frame rates. A theoretical comparison between these imagers, when applied to
localization based super-resolution microscopy, has been conducted by
Krishnaswami [120]. EMCCDs feature 90% quantum efficiency, but also
multiplication noise that effectively reduces this value by half [121]; their
noise performance is very uniform, due to the serial read-out, but the
achievable frame rate is low. sCMOS parallelizes the read-out and increases
the frame rate, at the expense of mismatches in the analog electronics. SPAD
imagers eliminate read-out noise with fully parallel digitization and can thus
achieve very high frame rates for binary (1-bit) frames. If multiple 1-bit
frames are added to achieve an 8 to 12 bit depth, which might be necessary or
not depending on the application, their frame rates become comparable to the
sCMOS ones. On chip counters will eliminate this bottleneck, enabling high
frame rates with higher bit depth. In addition, the high speed and picosecond
temporal resolution of TDCs connected to SPADs can open new avenues such as
video-rate localization super-resolution and multicolor imaging. The SPAD
digitization also limits the noise contributions solely to DCR (and
afterpulsing), and although DCR levels are acceptable for super-resolution
applications, the percentage of “hot” pixels needs to be further reduced.

##  10 Raman spectroscopy

Raman spectroscopy is capable of providing data on the chemical composition
and molecular structure of a compound in a non-destructive and label-free way
[122, 123, 124], with applications to both _in vitro_ and _in vivo_ tissue
diagnostics, minimal preparation, and without damaging the samples. This
“fingerprinting” technique relies on inelastic light scattering with vibrating
molecules, whereby Stokes-scattered Raman photons are red-shifted. It has seen
a surge of biophotonics applications in the last couple of decades [123],
although it typically suffers from a weak scattering cross section, leading to
long overall acquisition times (unless used for example in combination with
other sampling techniques that reduce the area to be interrogated, employing
line scanning, or multi-focal/widefield setups). As such, the Raman signal is
often overshadowed by the sample (auto)fluorescence itself; when working with
biological samples, it might therefore be of interest to move towards the NIR
range, where autofluorescence is weaker, although the resulting SNR needs to
be weighted by the corresponding reduction in the scattered intensity, which
decreases with the 4th power of the excitation wavelength. The use of coherent
techniques such as nonlinear anti-Stokes Raman Scattering (CARS), resulting in
blue-shifted radiation, can enhance the scattered signal and circumvent these
limitations, enabling for example rapid chemical imaging, at the price of
appropriate tunable, femtosecond laser sources [123]. Penetration in deep
tissues, which is of particular relevance for _in vivo_ medical diagnosis or
when looking at live cells growing in 3D cultures, can be enhanced with
recently developed methods such as spatially offset Raman spectroscopy (SORS)
and Transmission Raman Spectroscopy (TRS); the interested reader can consult
[125] for a recent review.

A downside of moving to the NIR to reduce the (auto)fluorescence background is
that the sensitivity of standard CCD/CMOS imagers decreases, and this is
obviously all the more true for the SPAD imagers in standard CMOS onto which
this review is primarily focused. However, SPADs allow the design of compact,
all solid-state detectors for Raman spectroscopes operating in time-resolved
mode, whether via very short gates, ideally in the 10-100 ps range given the
nature of the Raman signal (very fast emission compared to the fluorescence
background, which is typically in the ns range) [126, 127], or based on
timestamping, e.g. with TDCs; this mode of operation also allows to reduce the
DCR and enhance the overall SNR. Several linear SPAD-based systems have indeed
been developed in the recent past, usually comprising one or a few lines,
targeting initially applications such as mineralogy, and subsequently
biophotonics as well.

One of the largest reported SPAD arrays is a 1024$\times$8 system [128, 129,
13] which was applied with success to mineral samples, where the background
fluorescence is longer than for typical biological samples of interest to us.
It featured 1-bit counters and a global gate as short as 750 ps (with a
corresponding standard deviation of 120 ps), which could be shifted in 250 ps
steps. The use of a standard 0.35 $\mu$m CMOS process led to a maximum PDE of
9.3% with a median DCR of 5.7 kcps at 3 V of excess bias.

A different approach was chosen in [126, 130, 131], where a
2$\times$(4)$\times$128 SPAD array, again in a standard 0.35 $\mu$m CMOS
process, featured 4 sub-ns time gates during which the on-chip electronics
counts the number of detected photons. This architecture also allowed to
determine, in addition to the Raman signal, the level of the residual
fluorescence and DCR. The fill factor of each SPAD was 23%, the minimum time
gate width 80 ps, and its variation along the spectral axis (timing skew)
$\pm$17.5 ps for a nominal width of 100 ps; the effect of nonhomogeneities of
this order on the samples of interest for biophotonics applications (featuring
ns- and sub-ns scale fluorescence backgrounds), although small, were discussed
as well. The overall IRF (instrument response function) was reported as 250
ps, and Raman spectra of several drugs of interest were acquired with a ps
pulsed laser at 532 nm [132], enabling the authors to reveal previously unseen
Raman spectral features. The same underlying standard CMOS technology was used
to design a gated 4$\times$128 SPAD array featuring an additional 512-channel
3-bit flash TDC [133], which allowed high timing resolution measurements (78
ps, 10 ps standard deviation in the first four bins) at the beginning of the
3.5 ns range which it covered. Several SPADs were again employed at a given
spectral point, to reduce the impact of noisy pixels.

Further nonhomogeneity studies have been carried out by the same group,
including by means of a 256$\times$16 SPAD test array with two on-chip TDCs,
leading to a detailed investigation of the effect of DCR and PDE as well as
gate length variations and temporal skews. The latter can indeed play an
increasing role, with growing design complexity, on the derivation of the
Raman spectrum when targeting 100 ps accuracy levels [134, 135]. In general,
the spectra have been found to be subject to a larger deterioration, as could
be expected, when the fluorescence lifetimes and levels get shorter and
higher, respectively. An efficient post-processing method relies on the
calibration with a known smooth (over the wavelength) fluorescence background;
this approach is discussed in [136], using a high-precision TDC ($<$ 50 ps)
whose range exceeds the expected timing skew, and sampling as well a part of
the fluorescence spectrum.

The linear 256$\times$2 array by Krstajić _et al._ [23], which was already
mentioned in the FLIM section, has also been used for surface-enhanced Raman
spectroscopy (SERS), again within the PROTEUS project. Significant hardware
and software improvements of the same sensor are reported in [66], whereas
removal of fluorescent background signals (in addition to DCR) is illustrated
in [65].

##  11 Optical tomography

Near-infrared optical tomography (NIROT) studies absorption and scattering of
light in turbid media, e.g. biological tissue. Light that has propagated in
the medium is detected at its surface. The measurement process is performed at
multiple wavelengths to exploit the knowledge of the absorption and scattering
properties of the constituent absorbers. This prior knowledge can then be used
to make a 3D reconstruction of the distribution of those absorbers within the
medium; this enables, for example, the non-invasive determination of
oxygenation values, usually by working in the 650-850 nm window (enhanced
tissue penetration).

The corresponding image reconstruction inverse problem is, however, ill-posed
by nature, leading to a low spatial resolution (1 cm level) with existing PMT-
based NIROT system; these have seen little use in clinical settings, and are
difficult to upscale. The spatial resolution can be improved by increasing the
measured amount of information, i.e. ideally the amount of detectors, as well
as their temporal information in the case of time-domain systems. This has led
to interest in solid-state detectors and in particular in SPADs [137], either
as single devices, or in the form of SiPMs, potentially enabling contactless
setups and optimized reconstruction algorithms.

While SiPMs can indeed be combined to form a wide area detector [138],
separate TCSPC electronics is still required, and cost might also play an
issue. Another route has therefore explored fully integrated CMOS SPAD arrays
featuring high PDE and timing accuracy, as well as thousands of detection
channels. An overview of the topic is provided for example in [139, 38, 140],
which also include application examples based on the LASP 128$\times$128 TCSPC
array [16]. The latter employed 32 10-bit 98 ps column-based TDCs in a pulsed
(time-domain) setup, recording time-of-flight information.

An enhanced CMOS SPAD sensor optimized for NIROT is described in [141, 142],
targeted at addressing the slow acquisition time bottleneck of previous
implementations, which can lead to motion artefacts and decreased patient
comfort. This 32$\times$32 SPAD array, called Piccolo, employs 128 TDCs, based
on the concept of dynamic TDC reallocation to reduce the timing-related die
area whilst at the same time reducing the probability of photon pile-up. It is
implemented in a 180 nm process employing a large spectral range SPAD and
cascoded passive quenching circuit to boost the pixel’s PDE, reaching a PDP of
greater than 10% at 800 nm with a native fill factor of 28%, at a pixel pitch
of 28.5 $\mu$m. Initial results indicate a minimum image acquisition time of
3.8 s per source and wavelength pair. The sensor has been integrated into a
system combining a supercontinuum laser and an acousto-optical filter for
multi-wavelength excitation, as well as a fiber switch to obtain up to 24
source positions [143] (Figure 6(a)); phantom measurements are in good
agremeent with simulations (Figure 6(b)). Future implementations could make
use of a SPAD gating feature [144], in order to reduce the impact of the early
backscattered photons in certain illumination geometries.

Fluorescence molecular tomography (FMT) relies on (exogenous) fluorescent
molecules to enable the reconstruction of their concentration distribution in
3D, e.g. applied to small animals for pre-clinical cancer research, and
possibly over time to track dynamic phenomena. A hybrid MRI-FMT imaging system
has been demonstrated in [145, 146], with the imaging component based on the
RadHard2[96] 32$\times$32 photon-counting SPAD array. The results are shown in
Figure 6, whereby an overlap of the MRI and FMT images of a mouse tumor is
displayed in (c) and enlarged in (d). The corresponding findings were later
confirmed by the histological analysis on the tumor.

Figure 6: (a, b) NIROT camera system prototype and measurements vs. simulation
results for a phantom [143]. (c, d) Fluorescence molecular tomography (FMT)
image as overlap of the optical image obtained with the RadHard2 32$\times$32
photon-counting sensor with the corresponding MRI image [146]. C51 cells (a
colon cancer-derived cell line) have been implanted in the flank of a mouse. A
clear spread in the protease activity, indicated by the significant higher
fluorescence intensity in some parts of the tumor, is shown. (c) Complete
MR+FMT image, (d) zoom on cancer region. Images reprinted from [143, 146].

Finally, it is worthwhile to mention the possibility of using the exquisite
timing capabilities and spatial resolution of SPAD arrays to detect the
ballistic and snake photons which have travelled through tissue, such as
demonstrated in [147]. The MEGAFRAME32 sensor was employed to locate the
distal-end of a fibre-optic device deep in tissue, with cm resolution and in
clinically relevant settings and chest and lung models, as well as through the
entire thickness of a human torso.

##  12 Other biophotonics applications and sensor concepts

Amongst the several other biophotonics applications which have been the
subject of investigation with SPAD arrays, the nuclear medicine domain, and
PET in particular, play a prominent role. Its peculiar architectural
implications have already been analysed at the end of the Array architecture
section, and are particularly interesting because they basically span the full
pixel granularity range, from small (mm sized) silicon photomultipliers
(SiPMs) where all SPADs are connected together to provide a common output, to
imagers with individually addressable SPAD pixels. The former, which are
already employed in analog form in top-end PET systems, are being revisited,
e.g. monolithically with the addition of on-chip TDCs, while keeping backward
compatibility [148], or by coupling an off-the-shelf analog SiPM array to an
FPGA-based board to enable advanced timing functionality with relatively
simple hardware modifications [149]. The latter are being extended in 3D, as
will be described in the corresponding section below.

On the more future oriented side, the SPAD’s spatial and time-resolved
capabilities are being investigated to enable quantum-based superresolution
microscopy. In principle, using a quantum correlated N-photon state combined
with an optical centroid measurement (OCM) allows indeed to reach a resolution
enhancement of 1/N. Apart from the significant challenges on the source side,
such an approach calls for high detection efficiency, timing resolution and
frame rates on the detection side, while minimising crosstalk and DCR. An
early implementation of a monolithic 4$\times$4 G(2) SPAD imager was reported
in [150, 151], aimed at resolving second-order intensity correlations. More
recently, the SUPERTWIN project started looking at all solid-state
technologies for the generation and recording of entangled photons, targeting
a resolution of 40 nm. The much larger and advanced SPADnet1 SPAD array,
originally designed for PET applications, was employed in a first proof-of-
principle experiment to detect spatially entangled photon pairs, generated by
spontaneous parametric down-conversion (SPDC) in a non-linear crystal pumped
with an intense laser beam [152, 153]. The collected experience allowed to
proceed with the design of an _ad hoc_ detector, incorporating on-chip
features to increase the duty cycle (e.g. avoid reading empty frames). A
32$\times$32 SPAD array was manufactured in a 150 nm CMOS process, allowing 50
ns long observation windows at up to 800 kHz to measure 1st (G(1)) and 2nd
(G(2)) order correlation functions, and experimentally characterized again
with a source of entangled photon pairs [154].

A sensor concept which is alternative to the monolithic approaches described
so far, built around the use of “reconfigurable pixels”, has already been
hinted at at the end of the Read-out architecture section. The underlying idea
is to be able to reconfigure the main data processing features as a function
of the specific application needs, as is for example the case with the
LinoSPAD 256$\times$1 linear array. This system is designed in such a way that
the sensor layer hosting the actual SPADs (with fill factor of 43%) is
decoupled from the data processing features, which are embedded in a companion
FPGA. The latter contains core processing blocks such as a 64-element parallel
TDC array [50, 35] for time-correlated applications, whereas others are
reconfigurable and can in principle be combined in a modular fashion. Another
advantage of this approach resides in the possible combination of a sensing-
optimized layer with a more advanced processing tier (e.g. 40 nm or 28 nm), as
well as the possibility of exchanging one or the other as technology evolves
or new firmware is designed.

Figure 7: Recent SPAD concepts for imagers revolve around 3D integration,
possibly combined with microlenses to further maximize the fill factor. (a) A
3D integration concept image, (b) a two-tier implementation with additional
microlenses [155], and (c, d) cross sections of different imagers using three
tiers [156, 157]. Frontside illumination is used in (c), backside illumination
in (b) and (d). (b–d) Images reprinted from [155, 156, 157].

The combination of a top sensor layer with a bottom (likely all-digital CMOS)
control and processing layer, each optimized for the respective function, can
also be achieved with 3D-stacking techniques (see Figure 7(a) for a concept
image), which are progressively getting accessible to a larger user community
and which profit from the developments in consumer markets (e.g. cameras for
mobile phone applications), where significant resources are available. Such an
approach could potentially enable to reach high PDE, low DCR and reduced
jitter and afterpulsing, while at the same time adding advanced functionality
and low power consumption due to the use of smaller technology nodes in the
bottom tier. In addition, the use of compound semiconductors for the top
layer, such as InGaAs, could open up additional imaging windows in the NIR and
even mid-wave infrared, leading to enhanced tissue penetration (see for
example Figure 7(d) and [158, 156] for a summary of extensive work in this
direction by MIT’s Lincoln Laboratory). Another route to enhanced NIR
sensitivity when staying with silicon-based platforms is backside illumination
(BSI), similarly to what already implemented in most high-end CMOS consumer
imagers, whereby the substrate of the top wafer needs to be thinned down to
only a few micrometers; this can be combined with thicker active volumes to
counteract the reduced absorption of silicon in the NIR. In general, the price
to pay for going 3D is higher design complexity, challenging 3D (wafer level)
bonding techniques, and corresponding development costs.

Early work towards CMOS 3D IC SPAD-based imagers includes a proof-of-concept
400$\times$1 linear array [159, 140], specifically designed with NIROT
applications in mind, which combined 400 SPADs on the top tier with 100 TDCs
(50 ps LSB) on the bottom layer, in a 130 nm process. In parallel, the use of
silicon-on-insulator (SOI) was explored by [160, 161], with the aim of
fabricating an array of 32$\times$32 custom (BackSPAD) photodetectors in a 3
$\mu$m thick SOI film of an SOI wafer (0.35 $\mu$m feature size). The latter
is then flipped and wafer bonded to a second 0.35 $\mu$m CMOS wafer with the
ancillary electronics. A large body of work is available from Sherbrooke on 3D
specifically dedicated to PET, with the aim of achieving a possible “ultimate”
one-to-one coupling between each pixel and its corresponding quenching and
timing electronics [162, 163]. An important part of the effort was dedicated,
not surprisingly, to the 3D integration process itself, with proofs of
principle along the way such as [157] detailing the design of the top tier 0.8
$\mu$m high voltage CMOS SPADs in a frontside illumination approach (FSI, see
Figure 7(c)), and the corresponding challenges in terms of connections (use of
through silicon vias).

More recently, several papers report on work exploiting very advanced
commercially available 3D BSI technologies. They were in part driven by the
need for optimized, small pitch SPAD arrays with enhanced red and NIR
sensitivity for consumer applications such as LIDAR, and have led to SPAD
implementations that were unthinkable in such technology nodes just a few
years ago, also allowing potential extension to megapixel arrays. In [164] for
example, the pixel pitch was reduced to 7.83 $\mu$m leading to a
128$\times$120 time-gated prototype combining an imaging specific 65 nm top
tier with a 40 nm bottom tier employing 1-to-1 hybrid bond connections. A
different implementation in the same 3D IC CMOS process is reported in [165],
achieving higher PDE thanks to cascoded passive quenching and active recharge,
while still being compatible with the transistor operating voltage regimes of
such highly scaled technologies. The example in Figure 7(b) employs a 45 nm
sensor tier and 65 nm low-power processing tier, adding microlenses for fill
factor enhancement. Very low afterpulsing was obtained with a very short 8 ns
dead time. Finally, the top tier was further scaled down to 45 nm in [166],
while further ameliorating the peak PDP and reducing the jitter and DCR. The
latter stays in general 2-3 orders of magnitude higher than the best FSI
technologies, whereas the PDP, which is typically very low in the blue region
for BSI implementations, has improved to above 30% in the red, at fill factors
well in excess of 50%.

##  13 Summary

Figure 8(a) provides a schematic overview of how the key functionalities of
the SPAD-based imagers reviewed in this paper are related to the main
biophotonics applications to which they have been applied so far. The arrows
pointing towards the center provide a qualitative indication of the relative
importance of the different functionalities, whereby most applications have
seen the use of at least two of them (e.g. timestamping and gating), excepted
localization-based super-resolution microscopy (SRM), which has only needed
photon counting so far. PET is a somewhat special case, relying on _ad hoc_
architectures as discussed in the first paragraph of the previous Section.

Figure 8(b) shows the distribution of the total number of SPADs over time,
based on the data from LABEL:tab:summary_table, indicating a clear trend
towards larger SPAD arrays over the past decade. (c–e) illustrate the
distribution of some of the key figures of merit (total number of SPADs, PDE,
and DCR per unit area), using again LABEL:tab:summary_table as a reference,
grouped per main biophotonics application type. Each dashed line corresponds
to an individual sensor. For the total number of SPADs and the PDE, higher
values are preferred, whereas the contrary is obviously true for the DCR. The
size of each box is representative of the spread of the figures of merit for
that specific application; it is for example larger for FLIM, where the first
developments have taken place early on, when the technology and designs were
far from optimized. The box size also reflects the number of sensors which
have been employed so far; from this perspective, it is smaller for SRM, which
has only been addressed in the very recent past.

Finally, as discussed in the SPAD section the design of SPAD sensors entails
numerous trade-offs, such as PDE vs. DCR shown in Figure 8(f). This plot
indicates a move towards higher PDE values, although this is not easy to
achieve whilst still keeping low DCR (the ideal sensor would be placed in the
bottom right corner of this plot).

Figure 8: (a) Schematic overview of the SPAD-based system complexity, in terms
of key functionalities (counting/gating/timestamping) vs. the main
biophotonics applications. (b–f) Overview of representative SPAD sensor
figures of merit as a function of the main target applications, based on data
from LABEL:tab:summary_table: (b) Total number of SPADs (corresponding to the
effective spatial resolution in imaging arrays) vs. time; (c–e) Total number
of SPADs, PDE and DCR per unit area grouped per application type (dashed
lines: individual sensors, top/bottom of each box: maximum/minimum); (f) DCR
per unit area vs. PDE.

##  14 Conclusions and outlook

In this review, we have focused on SPAD imagers in standard CMOS technologies
and their biophotonics applications. Individual SPAD pixels and small arrays
have evolved from a scientific curiosity 15 years ago to a range of fully
integrated devices, where the key challenges have been sensitivity,
homogeneity, noise, reliability, and reproducibility.

At device level, the performance gap between SPADs fabricated in custom
technologies and in standard CMOS has been shrinking over time, whereby CMOS
can leverage 60 years of experience and investments in scalable and high-yield
technologies. The corresponding improvements have been significant in terms of
key parameters such as PDP and fill factor, with peak values in excess of
40–55%, to DCR, drastically reduced to below 0.1 cps/$\mu$m2 in the best
SPADs, as well as the percentage of hot pixels (just a few percent in the best
technologies). The spatial resolution has also increased to a quarter
megapixel for the largest formats, with megapixel arrays on the horizon. This
has called for a shrinkage of pixel size to well below 10 $\mu$m, with some
groups targeting SPADs of just a few microns in deep-sub-micron technology
nodes (45 nm and below) [167], not very far from their counterparts in CMOS
imagers; this feat was unthinkable just a few years ago. The timing accuracy
was already excellent (typically 50–100 ps, with the best SPADs in the 20–30
ps range), and has therefore evolved in a less spectacular fashion.

At the architectural level, standard CMOS SPAD arrays have a clear advantage
over CCDs/sCMOS imagers, when parallel single-photon counting and/or timing is
required; they can be coupled in a flexible way to different digital blocks
for data acquisition and/or processing, in close sensor proximity, thanks to
the natively digital SPAD data output. The absence of read-out noise is an
important issue which is often neglected, all the more so when SPAD imagers
are coupled to very high speed (about 100 kfps), ADC-less binary
implementations, delivering continuous microsecond frames in real-time to
capture fast transient phenomena. The capability of implementing integrated,
parallel nanosecond gating is also a very appealing alternative to non-all-
solid-state implementations.

It therefore comes to no surprise that FLIM has been explored first, together
with multi-beam approaches that make full use of the sensors’ native
parallelism. This was then followed by a host of other time-resolved
biophotonics applications, all the way to disruptive scenarios such as
quantum-based superresolution microscopy; the latter represents a good example
of a sensor architecture which has been custom-designed to meet specific
requirements, and for which efficient sensor alternatives are rare. It is
however true that most SPAD imagers are still research prototypes, and only
some are available commercially, e.g. from SMEs such as PhotonForce (UK) or
MPD (Italy), usually as a spin-off of designs explored in academic
environments. (Other commercially available SPAD arrays are usually derived
from non-imaging SiPMs, e.g. the Philips Digital Photon Counting dSiPM for
clinical PET, or the STMicroelectronics time-of-flight sensors [168], aimed at
ranging in the consumer market.)

This is partly due to the maturity of the sensors themselves and/or of the
underlying technology, and partly to the fact that the overall sensitivity, in
particular in the red, still lags behind those of CCDs/sCMOS imagers by
2–10$\times$. This can be an issue in applications where absolute sensitivity,
rather than SNR, is a must, or where the illumination power needs to be kept
low to avoid sample degradation. Certain applications do need a very high
timing resolution, typical of single, highly optimized SPADs, and can tolerate
longer acquisition times due to scanning. Finally, in certain cases SPAD
designers need to face competition from other single-photon or established
technologies.

In the future, we foresee that academic and research establishments will
continue pushing the state-of-the-art in terms of the key figures of merit,
targeting large format and high performance CMOS SPAD arrays, with industrial
applications starting to appear in the medium term. Larger industrial
conglomerates are more likely to emphasize high volume applications in the
mobile/consumer areas, e.g covering automotive, point-of-care, and Internet of
Things (IoT), possibly via smaller, dedicated units; these foundries might
also propose SPADs as IP blocks in a not too distant future. The resulting
consumer oriented developments are likely to follow trends similar to the ones
which characterized high end smartphone imagers (i.e. towards chip stacking
and 3D ICs); they might impact in a positive way niche markets in general, and
time-resolved applications of interest to the biophotonics community in
particular.

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