figure table

# Automated real-time spectral characterization of phase-change tunable
optical filters using a linear variable filter and infrared camera

David Bombara  University of Nevada, Reno, Department of Mechanical
Engineering, 1664 N. Virginia Street, Reno, NV, USA, 89557  Calum Williams
Department of Physics, Cavendish Laboratory, University of Cambridge, JJ
Thomson Avenue, Cambridge, CB3 0HE, UK  Stephen Borg  NASA Langley Research
Center, 1 Nasa Drive, Hampton, VA, USA, 23666  Hyun Jung Kim  NASA Langley
Research Center, 1 Nasa Drive, Hampton, VA, USA, 23666  National Institute of
Aerospace, 100 Exploration Way, Hampton, VA, USA, 23666

###### Abstract

Actively tunable optical filters based on chalcogenide phase-change materials
(PCMs) are an emerging technology with applications across chemical
spectroscopy and thermal imaging. The refractive index of an embedded PCM thin
film is modulated through an amorphous-to-crystalline phase transition induced
through thermal stimulus. Performance metrics include transmittance, passband
center wavelength (CWL), and bandwidth; ideally monitored during operation (in
situ) or after a set number of tuning cycles to validate real-time operation.
Measuring these aforementioned metrics in real-time is challenging. Fourier-
transform infrared spectroscopy (FTIR) provides the gold-standard for
performance characterization, yet is expensive and inflexible—incorporating
the PCM tuning mechanism is not straightforward, hence in situ electro-optical
measurements are challenging. In this work, we implement an open-source
Matlab®-controlled real-time performance characterization system consisting of
an inexpensive linear variable filter (LVF) and mid-wave infrared camera,
capable of switching the PCM-based filters while simultaneously recording in
situ filter performance metrics and spectral filtering profile. These metrics
are calculated through pixel intensity measurements and displayed on a custom-
developed graphical user interface in real-time. The CWL is determined through
spatial position of intensity maxima along the LVF’s longitudinal axis.
Furthermore, plans are detailed for a future experimental system that further
reduces cost, is compact, and utilizes a near-infrared camera.

###### keywords:

spectroscopy, phase-change materials, tunable filter, spectral imaging, linear
variable filter, GUI (graphical user interface)

*Corresponding author: Hyun Jung Kim<EMAIL_ADDRESS>
##  1 Introduction

Optical bandpass filters are critical components utilized in a plethora of
systems and applications, from fluorescence microscopy to remote sensing [1,
2, 3]. These filters are designed to transmit only a certain band of
wavelengths (passband) and block all others. Conventional optical bandpass
filters are passive—offering discrete static passbands, arising from the
interference of alternating index dielectric thin-films [4, 5]. There are a
growing number of imaging applications requiring precise spectral filtering
across a range of wavelengths (tunability), with motorized filter wheels
typically utilized [6, 7]. However, filter wheels are bulky, have limited
spectral coverage, and offer slow switching speeds. In recent years, there
have been significant research efforts toward cost-effective tunable optical
bandpass filters that can operate across multiple wavebands and exhibit fast
switching capability, based on nonstandard material platforms. Chalcogenide
phase-change materials (PCMs), and their integration into micro-and nano-
structured filter designs [8, 9, 10, 11], are emerging as an attractive
candidate for such a tunable filter. These exotic materials exhibit a large
refractive index shift across multiple wavebands through a reversible phase
transition (amorphous-to-crystalline) initiated through external energy
stimuli. When integrated into established or novel optical filter design
schemes—such as Fabry-Perot interference-based or metasurface-based
filters—PCMs can provide an optically active medium to tune the passband
center wavelength (CWL) through refractive index switching [10, 11, 8, 9].

Figure 1: Active wavelength tuning of the phase-change material (PCM) cavity
narrowband filter. (a) The PCM-cavity narrowband filter takes an optical input
(illumination) and returns an optical output that is filtered at a
corresponding center wavelength (CWL). The CWL is a function of the state of
the PCM. (b) The CWL of the optical output is shortest when the PCM is in its
amorphous phase, hence the lowest refractive index value. Partially
crystalline states increase the refractive index, hence the CWL, until the PCM
reaches the fully crystalline state (highest refractive index). (c) To “reset”
the PCM to the amorphous phase, a pulse of high power but short duration must
be applied (e.g., laser pulse). To set the material to its crystalline phase,
a lower-power pulse of longer duration must be applied. (d) The material
resets to its amorphous phase if it exceeds its melting temperature $T_{m}$,
then is rapidly quenched below its glass transition temperature
$T_{\mathrm{glass}}$ in time less than $t_{1}$. The crystalline transition
requires that the PCM exceed $T_{\mathrm{glass}}$ for a duration of at least
$t_{2}$.

PCMs undergo a phase transition through rapid localized melting and
recrystallization [12], which can be induced through optical or electrical
stimuli. The former is typically used in rewritable optical storage media
[13]. Compounds consisting of germanium, antimony, and tellurium (GeSbTe) are
commonly used for optical devices, from optical storage media, integrated
photonic elements, to tunable filters. To switch the material to its amorphous
phase, an ultrafast (nanosecond), high-intensity pulse must be applied. In
contrast, switching to the crystalline phase requires a longer, lower-
intensity pulse. For amorphization, the material must be heated above its
melting temperature, then rapidly quenched [10]. For the material to
crystallize, the material must be heated above its glass transition
temperature for a duration long enough to reach crystallization. PCM-based
tunable optical filters are typically switched using laser-based irradiation
[13]. An overview of the PCM filter concept, laser input sequence, and
corresponding temperatures of a PCM filter is shown in Fig. 1. Partial
crystallization can also be realized, and intermediate phase switching has
been previously shown to increase the number of accessible CWLs for a single
multistate tunable filter [11].

PCM-based tunable filters have been shown to provide all-solid-state ultrafast
(nanosecond) switching from the near-infrared (NIR) to mid-wave infrared
(MWIR) [11, 10]. Despite the promising results, no studies, to the knowledge
of the authors, have focused on device reliability metrics, which is crucial
to understand their behavior over many tuning cycles, device longevity,
failure rates, and suitability for practical and real-world applications. For
example, spectral properties that remain constant for each phase cycle would
indicate that the filter functions reliably. However, when its properties
begin to deviate from typical values, the filter may no longer be fit for its
purpose. To determine these reliability metrics and characterize the
tunability of the PCM-based filter, the spectral properties must be measured
after each phase change. These include the passband CWL, transmittance
intensity, and bandwidth (in terms of full width at half maximum, FWHM).
Further, good homogeneity across the spatial extent of the filter is highly
desirable, therefore the monitoring of this information is needed across the
entire filter (i.e., 1D spatiospectral information as opposed to single point
spectroscopic measurements). Fourier-transform infrared (FTIR) spectroscopy is
arguably the most common method for obtaining this information and is the most
accurate [10, 11]. For example, the Thermo Scientific™ Nicolet™ iS™10 FTIR
spectrometer has a 10,000:1 signal-to-noise ratio and spectral resolution of
0.4 cm-1 [14]. However, it is expensive and bulky instrumentation that
requires long data collection times to calculate the spectral properties from
the interferogram. Critically, it is challenging to integrate the PCM-based
filter switching systems within the FTIR apparatus and FTIR systems are
generally limited to providing spectral properties from only a single point on
the sample.

In this study, we present an approach to real-time PCM-based tunable filter
characterization that utilizes a linear variable filter (LVF) and IR camera to
measure the filter’s CWL. This method is simpler to construct for the research
community and enables in situ measurement across the entire filter area (1D
spatiospectral measurement). A graphical user interface (GUI) is developed in
Matlab® to control separate instruments and plot the tunable filter’s passband
CWL in real-time. These properties are measured based on acquired images from
an IR camera. Furthermore, we detail progress on the PCM’s reliability
evaluation and a system for tuning and characterizing the PCM using commonly
available, off-the-shelf hardware.

##  2 Experimental Characterization

FTIR based analysis, albeit accurate, is expensive, time-consuming (especially
if needed to perform spectral 2D measurements) and challenging to modify for
our real-time performance characterization requirements. Instead, our
alternate approach is to utilize a compact and inexpensive (relative to an
FTIR spectrometer) LVF—a small rectangular optical filter that has its CWL
varying linearly along the length of the filter [15]. Therefore, in
combination with the PCM-based filter, simply by imaging (recording) the
intensity response through an LVF, it is possible to characterize the spectral
performance. The accuracy of this method is high enough such that the phases
of the PCM (amorphous and crystalline) are unambiguously distinguishable and
the error margin of the CWL measurement is 0.085 µm. Error analysis is
provided in Section 2.2, “Experimental Results.” Our previous work realized a
PCM-based filter with a CWL of approximately 2.9 µm and 3.3 µm in its
amorphous and crystalline states, respectively [11]. With this LVF-based
system, the CWL shift of 0.4 µm is detectable. Further, this approach has the
advantageous feature that it is simpler to modify in order to evaluate (test)
other switching mechanisms or imaging approaches, such as trialing different
electrode designs, different laser technologies, off-axis illumination, etc.

Figure 2: Experimental setup. (a) A schematic of the experimental setup. The
infrared (IR) camera images the LVF with a fixed-wavelength bandpass filter
behind it. They are both illuminated by the contact hot plate (blackbody). (b)
A photograph of the experimental setup. The hot plate acts as a blackbody
thermal radiation source. (c) The LVF is mounted in the IR camera’s field of
view. (d) The bandpass filter is between the blackbody source and LVF.

###  2.1 Experimental Setup

To characterize a filter with tunable CWL, a system that validates the
spectral performance of static bandpass filters is first needed. The
experimental setup consists of (1) an IR camera (Indium antimonide detector,
FLIR SC8000 Series), (2) a radiation shield tube, (3) a contact hot plate
(blackbody radiation source), and (3) the filter stack (Fig. 2). The IR camera
is capable of imaging the MWIR waveband (2.5–5.0 µm) with a resolution of 1344
$\times$ 784 pixels. The camera utilized a lens with a focal length of 50 mm.
The distance between the lens and filter (target) was 122 cm. The IR images
are recorded using FLIR ResearchIR software. The blackbody radiation source
was an iron plate with emissivity $\varepsilon\approx 1$ and a diameter of
17.8 cm. A tube between the camera and filter stack shielded against unwanted
background radiation.

A top-down schematic of the experimental setup is shown in Fig. 2(a), whereas
the photograph of the entire setup is shown in Fig. 2(b). The LVF (Vortex
Optical Coatings, Ltd.) and bandpass filter are shown in Fig. 2(c) and 2(d),
respectively. The bandwidth of the LVF at 50% peak transmittance is specified
by the manufacturer to be 2% of the peak wavelength with peak transmittance
typically greater than 60% across the CWL band [16]. The LVF also has out-of-
band blocking within its spectral range of an optical density of 3.0 [16]. The
LVF has nominal dimensions of 15 mm $\times$ 3.5 mm $\times$ 0.1 mm. The CWL
of the passband ranges from 2.5 µm to 5.0 µm, depending on the longitudinal
distance along the filter.

The LVF is placed between the camera lens and as close to the bandpass filter
as possible, minimizing any deviation due to converging/diverging beams.
Similarly, the bandpass filter is placed between the LVF and the blackbody
radiation source. The bandpass filters are manufactured by Andover Corporation
and had CWL of 3.10 µm, 3.37 µm, 3.50 µm, 3.60 µm, 4.26 µm, 4.50 µm, and 4.70
µm. These filters each have a transmission of 75% and an out-of-band blocking
to an optical density of 3.0. The tolerance of the CWL of each filter is 40 nm
and each filter is 1.0 mm $\pm$ 0.2 mm thick. The bandwidths of the filters
are between 120 nm $\pm$ 30 nm and 140 nm $\pm$ 30 nm.

###  2.2 Experimental Results

Using the MWIR camera, the LVF was imaged with seven different bandpass
filters behind it. Figure 3 shows the experimental results. The superimposed
view of the IR intensity image and RGB image is shown in Fig. 3(a) for
illustration purposes. As shown in Fig. 3, the CWL of the LVF varies along the
length of the filter beginning at 2.5 µm and gradually increasing to 5.0 µm at
the opposite end. However, due to variations in the manufacturing of our
particular LVF, experimental data confirmed that it reached its 5 µm peak in
just 10 mm of filter length. The “scan line pixel number” in Fig. 3 refers to
the numbering of the camera’s pixels along the horizontal axis of the LVF. The
CWL here is defined to be the wavelength at which the transmittance is
maximum. “Detector counts” from the IR camera are used as a proxy for
transmittance; the location of peak detector counts is the location of the
peak filter transmission. To find this location on the LVF, a single
horizontal line of pixels is sampled in the center of the filter and parallel
to the long axis. In Fig. 3(b), the locations of peak transmission can be
identified as regions of higher relative intensity, where the brightness of
the pseudocolor image corresponds to the number of counts recorded during a
given detector integration time. For each bandpass filter, the particular
integration time was determined experimentally to obtain the high-contrast
image and depended on the temperature of the blackbody radiation source.

Figure 3: The results of the bandpass filter characterization using the LVF.
(a) An IR image of the LVF overlayed on top of a red-green-blue (RGB) image of
the filter stack that includes the LVF and bandpass filter. (b) The IR images
of the LVF when bandpass filters of different CWL are placed behind it. (c)
The normalized detector counts versus the scan line pixel number for each
bandpass filter, with CWL shown in the legend. (d) The CWL versus scan line
pixel number of the bandpass filter. The correlation is linear such that the
scan line pixel number can be used to predict the CWL of the optical filter.

In this experiment, we demonstrate that we can detect the CWL and accurately
measure its location along the LVF. The plot of normalized detector counts
versus scan line pixel number is shown in Fig. 3(c) for each bandpass filter.
The experimental results confirm that the LVF accurately indicates the CWL of
the filter that is placed behind. The correlation between CWL and the scan
line pixel number is shown in Fig. 3(d). The pixel number of the scan line
used as a proxy for the linear distance along the filter.

For our experimental setup and detector/optics combination, the minimum
horizontal spatial resolution for each pixel is approximately 0.341 mm. The
instantaneous field of view (IFOV) of our imaging system represents the spot
size on the LVF for an individual pixel and also corresponds to our estimated
spectral measurement uncertainty. Using a 50-mm lens in combination with a
1344$\times$784, 14-µm-pixel-pitch sensor, we were able to sample the 15-mm
LVF with approximately 104 pixels along the filter’s horizontal (longitudinal)
axis. As stated previously, the LVF did not use the full 15-mm length to cover
the 2.5-µm–5.0-µm wavelength span. We measured the 2.5-µm CWL shift on the LVF
in approximately 10 mm rather than 15 mm. This slightly reduced the spectral
resolution of the system. With our 10-mm (effective) LVF, we were seeing a
0.25-µm/mm shift spatially along the filter rather than an expected 0.17-µm/mm
shift.

When combined with the IFOV of our imaging system (0.341 mm), the spectral
measurement uncertainty of our experiment can be calculated to approximately
0.085 µm. A 0.341 mm spot size on the filter corresponds to a 0.085 µm change
in wavelength across a pixel. In Fig. 3(d), the CWL of the bandpass filter is
the independent variable whereas the scan line pixel number is the dependent
variable. If the CWL was unknown and the scan line pixel number was used to
predict the CWL, the prediction error may be $\pm$0.085 µm. This is because an
increase of 1 mm along the LVF’s length corresponds to a 0.250 µm increase in
the passband CWL. Similarly, an increase of 0.341 mm (the horizontal IFOV)
corresponds to an increase in the passband CWL of 0.250 µm/mm $\times$ 0.341
mm = 0.085 µm. The IFOV is a function of the target distance and camera
resolution. The measurement uncertainty may be minimized in future
implementations through a change in imaging optics: for example a camera lens
with a longer focal length, an image sensor with greater spatial resolution,
and a longer (> 15-mm) LVF. All of these elements would improve our system
accuracy by increasing the sampling of the LVF.

The location of the scan line, $y_{scan}$, is shown in Fig. 4. To find the
location of the CWL, first, a region of interest (ROI) is drawn over the
filter image. $y_{scan}$ was chosen to be in the center of the LVF. It was
initially assumed that the horizontal position of the peak transmittance may
depend on the chosen location of $y_{scan}$, due to fabrication tolerances.
However, Fig. 5 shows otherwise, which shows the variation of the detector
counts in the 2D pixel grid. Figure 5 shows that the location of peak
transmission along $x$ does not change depending on the chosen scan line
location, $y_{scan}$. Another method to determine the location of peak
transmittance would be to scan every pixel in the 2D ROI for every CWL
measurement. Compared to scanning a single line, this method is less
computationally efficient, making it less suited for the real-time
characterization application. The number of detector counts is evaluated
within two bounds, where $x_{lb}$ and $x_{ub}$ are the lower and upper bounds,
respectively. In Fig. 4(a), $x$ and $y$ refer to the numbering of the image’s
horizontal and vertical pixels, respectively. The detector counts must be
evaluated within bounds because the detector counts increase dramatically
outside the range of pixel numbers that do not cover the LVF. This increase
can be seen in Fig. 4(b), where beyond $x_{lb}$ and $x_{ub}$ the colors that
correspond to the detector counts appear brighter. Figure 4(b) shows results
for the 3.60-µm filter with labeled spectral properties. The locations of the
CWL, FWHM, and peak transmittance are shown. The CWL is a function of the scan
line pixel number; the two variables vary according to the equation,

$CWL=a(x-x_{lb})+\lambda_{0},$ (1)

where $a$ is the proportionality constant, $\lambda_{0}$ is the passband CWL
when $x=x_{lb}$, and $x$ is the numbering of the pixels horizontally. Least-
squares linear regression identified $a=0.04787\,\mathrm{\mu}\mathrm{m}$ and
$\lambda_{0}=2.4917\,\mathrm{\mu}\mathrm{m}$.

Figure 4: The method for determining the CWL of the filter from raw pixel
data. (a) The IR image used to obtain CWL. The scan line is located at
$y=y_{{scan}}$. The upper and lower bounds of the horizontal pixel number are
$x_{lb}$ and $x_{ub}$, respectively. (b) The spectral results of the 3.60-µm
bandpass filter. Spectral properties shown include the locations of the CWL,
peak transmittance, intensity half-maximums, and bounds of the filter. Figure
5: A three-dimensional plot showing the variation of the detector counts with
the horizontal and vertical pixel numbers that correspond to the area of the
IR image that the LVF occupies. As long as the scan line passes through the
entire length of the LVF, the location of peak transmittance does not depend
on the exact location of the scan line.

##  3 Automated Performance Characterization

###  3.1 Overview

The PCM tunable filters are based on thin-film Ge2Sb2Te5 and fabricated in a
laboratory at NASA Langley Research Center; the fabrication process is
described in detail in other studies [11, 10, 17]. The evaluation of tunable
PCM-based filters may be automated in two aspects: phase switching and
performance characterization. A Matlab®-based application is developed to
control the hardware systems and analyze incoming data. Figure 6 details the
main loop that runs in order to switch and characterize the PCM-based filter.
The source code for the Matlab® application is available at
https://dbombara.github.io/automated-pcm-characterization, as well as a
description of the experiment and setup.

Figure 6: Operation flowchart: The general commands and processes executed by
the Matlab® graphical user interface (GUI). The program runs in a loop until
the cycle limit is reached but can be terminated by the user at any time.

Once the user begins the experiment, the filter automatically switches between
the amorphous and crystalline states while its CWL is automatically calculated
and saved for each cycle. The sequence of functions that automates the system
is shown in Fig. 6. The GUI allows the user to conveniently control and
monitor the experiment’s progress. At the start of the experiment, Matlab®
establishes connections to each instrument. The initial IR image is then
loaded, with the ROIs displayed on the screen. At this point, the user may
redraw the ROIs, based on the exact position of the LVF in the view of the
camera. The selected ROIs are displayed on the screen and remain in view while
the experiment runs. Before firing a laser pulse, first, the desired state is
evaluated. Typically, the desired state alternates on each cycle between
amorphous and crystalline. The desired state of the PCM determines the
intensity and duration of the fired laser pulse. A closed relay switch
instructs the IR camera to save its image. The laser waveform is then plotted
for that cycle. Then, the file of the IR image is imported and displayed in
the GUI. An image mask is applied that allows the CWL to be calculated and
plotted within two bounds on the horizontal axis. All data from that cycle are
then saved to a file. The experiment runs until the cycle limit is reached,
which can be preset by the user or determined by the GUI based on the data
from each cycle. More details on these processes may be found below.

###  3.2 Phase Switching

The switching of the PCM between amorphous and crystalline states is triggered
by the heat of an incoming laser pulse. A Quantel Evergreen HP pulsed laser is
chosen in this study to deliver up to 340 mJ of energy at 532 nm center
wavelength. The pulses are delivered at a rate of up to 20 Hz. The wavelength
and energy level were chosen based on our previous work on GeSbTe phase-change
metasurface spectral filters [11]. The pulsed laser output is controlled with
a function generator and is viewed with an oscilloscope; this conveniently
enables the user to monitor the waveform. The laser consists of two
components: the power supply and the “head”, which emits the pulsed laser
beam. In this study, the oscilloscope (Tektronix MDO4024C) and function
generator (Tektronix MDO4AFG) functionalities are combined into a single
instrument (MDO stands for mixed-domain oscilloscope). This choice of
oscilloscope and function generator was due to the high-level programming
commands that are available in Matlab®.

The laser, function generator, and oscilloscope are controlled within the
Matlab® user interface. From Matlab®’s Instrument Control Toolbox™, Quick
Control Oscilloscope commands are employed to issue high-level commands to the
instruments. This aspect of the GUI builds upon the Oscilloscope App from
MathWorks® and incorporates similar functionality [18]. Serial commands can
also be issued directly to the laser to adjust the settings of the
instruments, such as the power level [19].

###  3.3 Performance Characterization

The performance characterization is conducted in a way similar to the
characterization in Section 2. To automate the image acquisition process, a
USB-controllable relay (8-Channel 5-Amp ProXR Lite, Relay Pros) triggers the
camera to record the IR images. In this study, the switch is “dry”; the
closure of the relay switch draws no electrical current. The FLIR ResearchIR
software saves the image in a proprietary file format known as ATS. Then, the
FLIR File Reader Software Development Kit (SDK) is used to import the ATS file
into Matlab®. Functions from the SDK then extract the image’s metadata and raw
pixel information so that the image can be displayed in the GUI. Using the
imported image, the CWL of the tunable filter is calculated in the manner
explained in Section 2, using Eq. 1.

###  3.4 GUI Design

The GUI is designed with the user’s inputs on the left quadrants of the window
and the system’s outputs on the right quadrants. A screenshot of the GUI is
shown in Fig. 7. The various aspects of the GUI are organized with tabs. The
names of the tabs include “Instruments”, “Measurement Mode”, “Stop Condition”,
“Pulse Settings”, and “Testing”. There are additional groups of tabs for
monitoring the performance characterization of the PCM by displaying raw data
and plotting spectral properties.

Figure 7: Real-time filter characterization: A screenshot of the custom
Matlab® GUI. The source code and packaged application are available at
https://dbombara.github.io/automated-pcm-characterization. The window contains
four quadrants (a)-(e), which each contain tabs. The left half of the GUI is
generally for inputs by the user, whereas the right half displays the output
from the instruments and computed spectral properties. (a) The top-left
quadrant of the GUI contains tabs for hardware-based settings and conditions.
(b) The top-right quadrant of the GUI displays “raw” data from the IR camera
and laser. (c) The Instruments tab, which is a part of the top-left quadrant,
enables the user to connect to the different instruments. (d) The bottom-left
quadrant is for software-based settings. (e) The bottom-right quadrant
displays the spectral properties of the tunable filter. With the current LVF-
based approach, only the CWL may be plotted. In future work, other properties,
such as transmittance and full width at half maximum (FWHM) will be
calculated. The (f) Stop Condition tab, (g) Testing tab, and (h) Files tab
allow the user to have more control over the experiment.

####  3.4.1 Instruments Tab

It is crucial to ensure all instruments are properly connected before
beginning the experiment. For that reason, the Instruments tab, shown in Fig.
7(c), includes information to connect and operate the oscilloscope and
function generator. Options such as the resource (the instrument identifier),
driver, channel, voltage range, and acquisition time are selected by the user.
The Instrument tab also contains buttons for connecting to the relay and laser
power supply via serial connections.

####  3.4.2 Measurement Mode and Stop Condition Tabs

The Measurement Mode tab (Fig. 7(d)) and Stop Condition tab (Fig. 7(f)) both
allow the user to select options for terminating the experiment when a
particular condition, or combination of conditions, is met. These tabs are
especially useful for hours-long reliability tests. In the Stop Condition tab,
the user can choose to stop the experiment when either the transmittance, CWL,
or both properties fall outside a predetermined limit. Similarly, the
experiment may stop after a particular number of occurrences outside that
limit, or when the number of occurrences reaches a set percentage of tuning
cycles completed thus far. In contrast, the conditions in the Measurement Mode
tab—total cycles and total time—are calculated in the GUI itself without
relying on the connected hardware.

####  3.4.3 Pulse Settings Tab

The required energy and pulse duration may change depending on the particular
PCM that is under test. In the Pulse Settings tab (Fig. 7(a)), the user can
set the desired laser voltages (V), pulse widths (ns), and delays (µs) between
pulses. The GUI then calculates the duty cycles (%), frequencies (MHz), and
periods (µs) of the pulses for amorphization and crystallization. The specific
values of the laser voltage, pulse width, and delay are determined
experimentally and vary depending on the particular materials utilized in the
tunable filter.

####  3.4.4 Tabs for Monitoring Raw Data

The top-right quadrant (7(b)) of the GUI window is for displaying “raw” data:
in general, data that come directly from instruments, not values that are
computed as a result of the instruments’ data. In this quadrant, there are two
tabs: the “Laser (Osc/FGen)” tab and the IR Image tab. As the names imply, the
former plots the input to and output from the laser, as determined by the
function generator and oscilloscope. The input signal to the laser differs
from the output signal; the laser requires two pulses of 5-V amplitude and
10-µs duration, with 170 µs between pulses. However, the output pulse from the
laser head is only nanoseconds-long. The IR Image tab displays the IR image
and ROI that are selected before the experiment. As the CWL of the PCM-based
filter is tuned, the IR image will change based on the indicator on the LVF.

####  3.4.5 Tabs for Testing and Performance Characterization

The Testing tab (Fig. 7(g)) contains buttons for the user to test the
functionality of the GUI before running the experiment. The relays can be
turned on and off with the click of a button. Sample files can also be
imported, after which the IR image can be plotted, and the spectral properties
can be calculated. The Files tab (Fig. 7(f)) allows the user to specify file
and folder names from which data will be imported and to which the data will
be saved.

The bottom-right quadrant (Fig. 7(e)) of the GUI window contains plots of
values computed from the IR image above. The cycle number is plotted on each
abscissa whereas the CWL, FWHM, and transmittance intensity are each plotted
in separate tabs on the ordinate axes.

The Matlab® GUI has much utility in the current experimental system, but in
future work, the GUI will be applied to (1) an experiment to investigate the
PCM-based filter’s reliability and (2) a compact, and custom-designed circuit
to optically switch the PCM-based filter and characterize its phase changes in
the NIR waveband.

##  4 Applications

Despite the current capabilities of the automated experimental system, future
developments are necessary to make PCM-based filters ready for commercial and
government applications. By building upon the Matlab® GUI and IR camera-based
characterization in this work, two additional developments are proposed.
First, experiments will be conducted to evaluate the reliability and longevity
of the PCM-based filter. Second, a system to characterize and tune the PCM-
based filter in the NIR waveband will be developed with commonly-available and
cost-effective components. In addition, the proposed system will have reduced
size and weight due to a custom-designed circuit for optically switching the
phase of the PCM.

###  4.1 Reliability Experiment

The reliability of the PCM-based filters will be evaluated using the Matlab®
GUI in future experiments. Figure 8 shows the schematic of the reliability
test that will utilize the Matlab® GUI. The setup is similar to that in Fig.
2, but with added components to facilitate the laser-based tuning of the PCM-
based filter. Because the laser will heat the PCM-based filter during the
phase change, the IR camera must wait for that heat to dissipate before taking
an image. This is because the IR camera cannot differentiate between surface
heating of the filter and blackbody radiation. During the reliability
experiment, the laser firing and the IR image acquisition will be separated
for a duration that allows the heat of the PCM to sufficiently dissipate.
However, the experiment will still be automated, since the filter may be
potentially be switched for $10^{3}$–$10^{6}$ cycles until it fails; the
moment of failure can be identified with our approach. Previous PCM
reliability studies have demonstrated long operational longevity, with one
example demonstrating $1.4{\times}10^{8}$ switching cycles before failure
[20]. However, PCM-based optical filters face challenges over their resistive-
based PCM counterparts. In optical PCM-based devices, the entire area of the
tunable film must undergo a complete and reversible phase transition [12].
However, resistive-based PCM-based devices do not require complete
crystallization of the tunable film to switch from “high” to “low” [12].

Figure 8: The reliability test setup: The reliability test will rely on the
Matlab® GUI for automated laser control and automated measurement of the
filter’s CWL. The CWL will indicate the current state of the filter.

To be used in NASA’s Space and Science missions, the filter must be reliable,
with any drop-off in performance as a function of the tuning cycle known in
advance. For example, one potential application of the tunable filter would be
the Space Launch System (SLS) for the Artemis-1 Mission. The filter could
enable accurate temperature measurement of the rocket’s core booster stage via
radiometric techniques because of its nanosecond-scale filter switching speeds
and narrow bandwidth. If the error margin of the temperature measurement is
reduced, the maximum temperatures from the rocket may be smaller than
originally designed for, therefore, the heat shield may not need to be as
massive. The tunable filter could also be utilized for chemical and gas
sensing in NASA’s Science missions. If the mission time is one year, for
example, the tunable filter must work reliably for that duration.

###  4.2 Cost-Effective and Compact Tuning System

Despite the advantages of PCM-based filters, there is a need for decreased
size, weight, power, and cost; this would enlarge the range of possible
applications for the tunable filter. Figure 9 shows the design of a cost-
effective and compact tuning system for PCM-based filters that have detectable
phase transitions in the NIR waveband, albeit with typically an increased
absorption. The cost of components for this system was less than $400. The
schematic is shown in Fig. 9(a), the photographs of the initial build are
shown in Fig. 9(b)–(c), and the comparison of input and output signals are
shown in Fig. 9(d)–(e). The system consists of two main subsystems: the phase-
switching subsystem and the imaging subsystem.

Figure 9: The cost-effective and compact tuning system. (a) The schematic of
the proposed system. (b) A prototype and (c) the wired connections on the
breadboard. (d) The system takes an input pulse wave from the Arduino
microcontroller. (e) The output pulse waveform is amplified nearly 30 times
compared to the input pulse, with minimal distortion.

####  4.2.1 Phase-Switching Subsystem

In Fig. 9(a), the DC power supply (LRS-350-24, MEAN WEll Enterprises Co.,
Ltd.) provides power to the overall system and allows the laser diode to emit
high-current pulses. A programmable voltage regulator determines the input to
the step-up DC-DC converter (FS02-12, XP Power). The output of the voltage
regulator (LM317, Texas Instruments) is adjusted using a digital potentiometer
(X9C104). The amplitude of the DC-DC converter output determines the amplitude
of the laser pulse.

The microcontroller (Due, Arduino) functions as both a waveform generator and
controller for the digital potentiometer. A common-emitter amplifier circuit
is utilized to amplify the pulse wave from the Arduino. In Fig. 9, the values
of the resistors and capacitors are chosen based on circuit simulations. It is
noted that other amplification strategies may be used, such as an operational
amplifier with a high slew rate. A previous project created an arbitrary
waveform generator using an Arduino Due and obtained pulse widths as low as 12
ns [21]. Similarly, the Arduino in Fig. 9 was programmed to output a wide
range of pulse widths and frequencies. The output from the signal amplifier
determines the pulse width and frequency of the laser diode’s output. Although
the prototype was constructed on a breadboard, the system may be constructed
on a printed circuit board (PCB) to for more compactness. This prototype was
tested using one-Watt LEDs to approximate the behavior of a high-powered laser
diode.The components for the prototype are available from popular retailers,
making them accessible for both research and education purposes.

Preliminary results from the prototype are shown in Fig. 9(d)–(e). Figure 9(d)
shows the input pulse wave from the microcontroller whereas Fig. 9(e) shows
the output of the DC-DC converter. The input signal has a frequency of 4.98
kHz and a peak-to-peak voltage of 3.64 V. The output signal retained the same
frequency as the input signal, but its peak-to-peak voltage was 97.6 V. As
seen in Fig. 9(d)–(e), the signal is minimally distorted after amplification.

####  4.2.2 Imaging Subsystem

For the imaging subsystem, a single-board computer (Model 3 B+, Raspberry Pi)
is proposed to interface with an NIR camera (NoIR Camera, Raspberry Pi). This
camera is similar to the Raspberry Pi V2 Camera Module, except the NoIR camera
possesses no IR-cutoff filter. The spectral response of the Raspberry Pi V2
Camera Module has previously been studied [22],and its CMOS image sensor (Si-
detector) has a spectral response from approximately 400–1000 nm. The utility
in the Raspberry Pi NoIR camera is in imaging PCMs that exhibit index
modulation in the NIR as well as the short-wave infrared (SWIR) and MWIR.
Thus, phase transitions can be inferred based on the NIR intensity changes.
Using the NIR camera, Fresnel reflections of the PCM at normal incidence would
indicate the phase transitions, due to the PCM’s changing refractive index
upon switching. The Raspberry Pi computer and camera are directly controllable
using the Matlab® Raspberry Pi Support Package, making their integration into
the current Matlab® GUI straightforward.

##  5 Discussion and Conclusion

PCM tunable filters are emerging as an alternative technology platform for
ultrafast spectral filtering across a wide range of operating wavelengths.
However, the ability to accurately and easily characterize their performance
remains challenging. In this study, a system for automated tunable filter
characterization was designed and implemented as an alternative to FTIR
spectroscopy. The LVF was shown to accurately indicate the CWL of the bandpass
filter behind it. A Matlab® GUI was designed to automatically control the
subsystem instrumentation, display the filters’ spectral properties, and
record the output data. The overall system enables 1D spatiospectral (bright
field) characterization and allows for easier integration of other tuning
mechanisms in comparison to FTIR systems. Plans were detailed for a future
reliability experimental system that is compact, low-cost, and utilizes a NIR
camera.

Future work will build upon the Matlab® application and experimental results
to advance the state of the art in PCM-based optical filters. Reliability
studies in the future will reveal the filter’s long-term performance. The
compact system for imaging and switching of the filter will decrease the cost
of optical PCM-based devices. This work has wider applications in systems that
utilize spatially-varying optical filters, such as circular variable filters.
The software that was developed in this work can also be modified to
incorporate more-sophisticated image processing algorithms, such as an
algorithm to automatically detect the location of the LVF and determine the
ROI. For devices and materials with more complicated spatially-varying
spectral profiles, machine learning techniques may be used for more robust
spectral characterization. The Matlab® application is available at
https://dbombara.github.io/automated-pcm-characterization.

###  5.1 Paper author contributions

D.B. developed the Matlab® application. H.J.K and C.W. conceived the idea.
H.J.K. and S.B. carried out the experiments. C.W. advised the device
characterization. All authors wrote the manuscript with D.B. as the lead.

###  5.2 Acknowledgments

D.B. would like to thank the NASA Internship, Fellowship, and Scholarship
(NIFS) program coordinators and/or project manager for the internship
opportunity. The authors would also like to thank Scott Bartram, Equipment
Specialist at the NASA Langley Research Center, for his expertise and
assistance with laboratory instruments. Beginning in June 2020, this work has
been completed under the NIFS program with the project entitled “LabVIEW
controlled evaluation system of actively tunable filter for NASA Science &
Space Mission”.

###  5.3 Funding statement

Langley Research Center (CIF, IRAD); Engineering and Physical Sciences
Research Council (EP/R003599/1); Wellcome Trust (Interdisciplinary
Fellowship).

###  5.4 Competing interests

The authors declare that they have no competing interests.

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David Bombara is a master’s student in mechanical engineering at the
University of Nevada, Reno. He is also an intern since June 2020 for the NASA
Langley Research Center through the NIFS (NASA Internship, Fellowship, and
Scholarship) program. He obtained his bachelor’s degree in mechanical
engineering from the University of Nevada, Reno in 2020. His broad research
interests include optical instrumentation, measurement, and control systems.

Dr. Calum Williams is a Research Fellow in the Department of Physics at the
University of Cambridge. His research focuses on miniaturized optical imaging
technologies for biomedical diagnostics. He completed his Ph.D. in
nanophotonics and holography at the University of Cambridge in 2017.

Stephen E. Borg is an aerospace technologist at the NASA Langley Research
Center. He received bachelor’s degrees in mechanical engineering (1988) and
physics (1988) from Old Dominion University and has been working primarily in
the areas of radiometry, infrared imaging, and temperature measurement in
support of NASA’s subsonic & hypersonic aeronautics research programs.

Dr. Hyun Jung Kim is an Associate Research Fellow at the National Institute of
Aerospace, working at the NASA Langley Research Center (LaRC) from 2009. She
received her Ph.D. in Materials Science and Engineering at the KAIST. She
works on solid-state tunable filter and telescope lenses, and is the PI of
LaRC’s ISO5 Optical Cleanroom. Her research interests include film process and
new material development to enable the next generation of optics, sensors, and
energy devices.