# Robust electrothermal switching of optical phase change materials through
computer-aided adaptive pulse optimization

###### Abstract

Electrically tunable optical devices present diverse functionalities for
manipulating electromagnetic waves by leveraging elements capable of
reversibly switching between different optical states. This adaptability in
adjusting their responses to electromagnetic waves after fabrication is
crucial for developing more efficient and compact optical systems for a broad
range of applications including sensing, imaging, telecommunications, and data
storage. Chalcogenide-based phase change materials (PCMs) have shown great
promise due to their stable, non-volatile phase transition between amorphous
and crystalline states. Nonetheless, optimizing the switching parameters of
PCM devices and maintaining their stable operation over thousands of cycles
with minimal variation can be challenging. In this paper, we report on the
critical role of PCM pattern as well as electrical pulse form in achieving
reliable and stable switching, extending the operational lifetime of the
device beyond 13,000 switching events. To achieve this, we have developed a
computer-aided algorithm that monitors optical changes in the device and
adjusts the applied voltage in accordance with the phase transformation
process, thereby significantly enhancing the lifetime of these reconfigurable
devices. Our findings reveal that patterned PCM structures show significantly
higher endurance compared to blanket PCM thin films.

Parth Garud1,2*, Kiumars Aryana1*, Cosmin Constantin Popescu3, Steven Vitale4,
Rashi Sharma5, Kathleen Richardson5, Tian Gu3,6, Juejun Hu3,6, Hyun Jung Kim1

1NASA Langley Research Center, Hampton, VA 23681-2199, USA
2Daniel Guggenheim School of Aerospace Engineering, Georgia Institute of
Technology, Atlanta, GA 30332, USA
3Department of Materials Science and Engineering, Massachusetts Institute of
Technology, Cambridge, 02139, MA, USA
4Lincoln Laboratory, Massachusetts Institute of Technology, Lexington, MA
02421, USA
5The College of Optics and Photonics, Department of Materials Science and
Engineering, University of Central Florida, Orlando, FL 32816, USA
6Materials Research Laboratory, Massachusetts Institute of Technology,
Cambridge, 02139, MA, USA

*These authors contributed equally.

Email<EMAIL_ADDRESS><EMAIL_ADDRESS>
##  1 Introduction

####

Achieving post-fabrication tunability in micro-optical devices is of broad
interest to applications spanning adaptive optics, beam steering,
multispectral imaging, heads-up display, and beyond. Such tunability has been
implemented with methods such as mechanical stretching[1], thermo-optic
modulation[2], liquid crystals[3], and magnetic field biasing[4] in various
active materials. Among them, chalcogenide-based phase change materials
(PCMs), a class of active materials exhibiting significantly different
optical, electrical, and thermal properties between their crystalline and
amorphous states[5, 6, 7], have garnered significant attention due to their
non-volatile phase transformation and large contrast in their refractive index
($\Delta n\sim 1-2$)[8]. In particular, PCM-based optical metasurfaces have
been shown to offer large tunability and allow for pixel level control, a
feature not possible with mechanical switching[9].

Metasurfaces are a class of optical components that have emerged as a driving
force in modern optical technology due to their unparalleled capabilities in
manipulating light in a more compact configuration. These ultrathin planar
structures, composed of sub-wavelength elements, have redefined conventional
optical devices by enabling precise control over light’s amplitude, phase, and
polarization in a remarkably compact configuration[10]. The compactness and
versatility of metasurfaces have propelled them to the forefront of optical
engineering, offering solutions to a myriad of challenges in various
applications. Today, they are poised to revolutionize optical technology,
finding applications in reconfigurable logic gates[11], optical computing[12],
neural networks[13, 14], optical filters[15, 16, 17, 18], beam steering[19,
20, 21], and focusing lenses[20, 21, 22, 23, 24].

Furthermore, the ability to change the properties of the devices without a
constant supply of current lowers the power requirements of PCM-based designs.
These materials have a rich history, finding early application in optical
discs due to their large optical contrast between amorphous and crystalline
phases in response to controlled heating stimuli[25]. Several techniques, such
as electrical[26, 27, 28, 29] or optical pulsing[23, 30, 31] and thermal
annealing[32, 33] have been employed to trigger phase transformation in PCMs.

The integration of PCMs into micro-optical devices such as metasurfaces offers
promising avenues in optical manipulation and signal processing[34, 18, 35,
36, 37], holding great potential for developing compact, lightweight, and non-
volatile tunable devices. This is particularly critical in space explorations,
where size, weight, and power (SWaP) considerations are crucial, and highly
tunable optical devices could enhance the efficiency of filtering, focusing,
and steering light. Despite PCMs being predominantly utilized in small-scale
devices like sub-micron memory cells, their broader application requires
scaling these devices to hundreds of microns or larger. However, the process
of scaling up PCM-based devices presents challenges in achieving reliable
phase switching. This requires high temperatures, exceeding 600°C for
amorphization, as shown in Fig. 1(a), with fast switching timescales on the
order of microseconds. As the device size increases, achieving a uniform
heating profile across the PCM area becomes an intricate task[38, 39]. The
pursuit of reliable and rapid phase switching, while mitigating thermal damage
to the device, remains a complex challenge[18, 40].

Figure 1: (a) Phase transformation mechanism and corresponding pulse shape for
crystallization including priming pulse to create nucleation areas [41] and
amorphization pulse, (b) image of the device showing different PCM geometry on
a single 200 $\upmu$m $\times$ 200 $\upmu$m microheater, (c) a cross-section
schematic of the device and the corresponding layers, showing the flow of
charges between the electrodes, (d) mechanism of phase transformation in PCMs
and the necessary temperature rise for each switching event, (e) pulse
optimization control unit.

In this study, we demonstrate robust switching of PCM-based devices through a
computer-aided algorithm that controls the heating pulses with respect to the
optical changes in the device, and study the impact of PCM patterns on their
switching characteristics. While these devices are not metasurfaces,
investigating the behavior of different geometries can provide insight into
improving the performance and damage resistance of metasurfaces. For this,
three device geometries were investigated: (i) 40 $\upmu$m $\times$ 40
$\upmu$m blanket-coated thin-film PCM, (ii) 40 $\upmu$m $\times$ 3 $\upmu$m
and 40 $\upmu$m $\times$ 2 $\upmu$m line pattern, and (iii) 2 $\upmu$m
circular dot arrays as depicted in Fig.1(b). All the geometries are deposited
on IR transparent microheaters with different sizes 150 $\upmu$m $\times$ 150
$\upmu$m and 200 $\upmu$m $\times$ 200 $\upmu$m. This enables us to evaluate
the performance of all the configurations under an identical operating
condition.

In order to deliver heat and trigger phase transformation, we utilize a doped-
Si microheater beneath the PCM that heats up through resistive heating upon
application of an electrical pulse. The cross-section of the device
configuration is shown in Fig. 1(c). We can control the temperature of the
microheater by adjusting the pulse width and amplitude, and thus control the
degree of phase transformation in PCM. In this study, we use amorphization
pulses that are on the order of 10 $\upmu$s, while crystallization pulse are
lower in amplitude but longer in duration, on the order of 0.5-1 s as shown in
Fig. 1(d). Additionally, we explore the impact of priming pulses—consisting of
6 consecutive pulses, each 200 ms wide, gradually increasing from 10 V to the
crystallization voltage—on the acceleration and uniformity of crystallization
within these devices [41]. The performance of the device highly depends on the
degree and quality of phase transformation, which is primarily dictated by
electrical pulses delivered at the heater. As a result, it is consequential to
optimize the pulses in a way that we achieve consistent phase transformation
upon each cycle, while avoiding damage to the device by over-heating.
Additionally, PCMs can experience drifts in their properties as they undergo
thousands of switching cycles, which causes the optical performance of the
PCM-based devices to deviate from their designed states. Thus, it is important
to adapt techniques to mitigate this drift in the switching property as the
device cycles.

Thus far, there have been limited studies devoted to streamlining the
optimization of PCM switching pulse parameters for devices beyond several tens
of micrometers, which has largely relied on a trial-and-error process
necessitating significant human intervention. These pulses are dependent on
the geometry of the microheaters as well as its neighboring materials and thus
can vary from one study to another. Manually testing the PCM-based devices to
determine the optimal pulses is a time-consuming process, starting from a safe
voltage and steadily increasing the amplitude or pulse width to reach maximum
switching. In PCM-based devices, it is crucial to apply precisely enough
energy to reach the melting temperature to avoid overheating the devices which
can lead to premature failure. This process is also prone to human error
during manual testing, for example causing damage upon increasing the voltage
by large increments. This can drastically decrease the lifetime of the device.

Although switching cycles in storage-class memory easily exceeds millions of
cycles[42], effectively switching PCM beyond several thousand cycles becomes
increasingly challenging as the effective switching size of PCM increases,
primarily due to exacerbated temperature non-uniformity across the volume of
the PCM. In this paper, we strive to achieve consistent and repeatable
switching events over thousands of switching cycles by focusing on the PCM
pattern geometry as well as pulse optimization through a computer-aided
algorithm. This technique not only automates the measurement procedure but
also gives feedback to the function generator to adjust the pulses according
to the device’s behavior. As we will show in this paper, the automation
process is critical for ensuring the reliable and reproducible operation of
these devices. For this, we leverage a feedback loop that monitors the changes
in the optical properties of the device using a short-wave infrared (SWIR)
camera and cautiously adjusts the pulse voltage to maximize optical contrast
while mitigating damage. This feedback-driven approach offers a promising
solution to the challenges associated with PCM-based micro-optical devices,
advancing their capabilities to new heights.

##  2 Method

###  2.1 Device Fabrication

####

The doped silicon heaters were fabricated at the MIT Lincoln Laboratory and
details of the fabrication process can be found in our prior works [43, 44].
In short, silicon-on-insulator wafers with 1 $\upmu$m buried oxide and 155 nm
of silicon were doped via ion implantation before metalization with a Ti/TiN
liner and 200 nm of Al layer for electrodes. The chip was then pre-patterned
via photolithography and the phase change alloy Ge2Sb2Se4Te (GSST) [45, 46]
was deposited via thermal evaporation and patterned using lift-off. The
thermal evaporation was performed at a base pressure below 2 $\times$10-6 Torr
at a maximum deposition rate of $\sim$8 $\mathrm{\mathring{A}}$/s The
deposition was performed from pre-weighted source materials prepared using a
melt-quench technique and crushed into powders. The target thickness was 180
nm. After the lift-off in N-methylpyrrolidone (NMP), the GSST patterns were
capped at 150 °C in 20 nm of atomic layer deposited Al2O3 and further
encapsulated via reactive sputtering with 800 nm of SiNx. Fluorine-based
reactive ion etching was used to open windows on the contact pads so the
aluminum can be accessed again for wire-bonding and testing.

###  2.2 Experimental Setup

####

In order to monitor the device as it undergoes phase transformation, we used a
SWIR camera (FLIR A6262) that operates in the wavelength range of 600 nm to
1700 nm. We used a long pass filter at 800 nm to only capture the IR portion
of the signal. For magnifying the image of the microheater, we used a Navitar
zoom lens with a co-axial illuminating port supported by a 10X Mitutoyo
objective focusing lens. For this experiment, we monitored the changes in the
reflected light from the PCM. For phase transformation, due to the large size
of the device compared to PCM-based memory devices, we needed to apply
voltages up to 40-50 V to trigger amorphization. For this, we used a function
generator (Agilent 33220A) that was coupled with a DC power supply (Keysight
E36232A) through a MOSFET switch that allowed us to deliver voltages up to 60
V. We controlled the applied voltage with a computer-aided algorithm that
captured an image from the surface of the device after each switching event
and analyzed the degree of the phase transformation. Additionally, several
other instruments such as a multimeter and oscilloscope were used to monitor
the resistance of the heater as well as the pulse shape. Ultimately, all the
components and instruments worked cohesively to cycle the device and monitor
its health and performance. Additional information can be found in Supporting
Note 01.

###  2.3 Graphic User Interface (GUI) Development and Pulse Optimization

####

The experimental procedure for studying phase transformation behavior in PCM-
based devices employs an iterative process coupled with real-time data
analysis to regulate pulse amplitude in relation to the extent of phase
transformation. In this experiment, prior to sending each pulse, an image of
the device is captured and analyzed within the software to determine the pulse
amplitude in the following cycles. Starting with user-defined input parameters
like pulse widths, amplitudes, and inter-pulse intervals, the software
initiates the cycling process. This involves administering priming and
crystallization pulses followed by an amorphization pulse to achieve a full
cycle.

The software employs a systematic analysis that relies on detecting changes in
the reflectance of the PCM-covered surface on the microheater, indicative of
the degree of phase transformation in PCM. Linear regression, represented by
coefficients $y=m_{1}x+m_{2}$, is used to identify trends linked to changes in
reflectivity, followed by sending signals to power supply to adjust subsequent
pulse amplitudes accordingly. The software updates the pulse parameters every
14 switching events, or 7 cycles. During each set of 14 switching events, the
software evaluates the reflectance trends for both amorphous and crystalline
states. It then adjusts the subsequent pulse based on the slope of these
trends. The software adjust the pulses every 7 cycles either by only 0.25 V.
This number is arbitrarily selected so that it is sufficiently small to stop
damage before it propagates further into the device. The software also checks
mean reflectance values and flatness of the mean switching value to identify
regimes of consistent reversible switching. Once such a state is achieved, the
pulse amplitudes are increased until a goal $\Delta$R is achieved. This goal
for the reflectance contrast is user-specified reflectance contrast.

One crucial parameter for computer-based identification of phase
transformation is the reflectance contrast, denoted as $\Delta R$, which is
determined by subtracting the amplitude of the response from each pixel in the
camera’s image in the amorphous phase from that in the crystalline phase. The
user can define a target value for how much switching (in counts) is desired
per cycle. This target value generally comes from prior tests and how much
change in reflectance could be achieved for a given PCM composition. If this
number is too small, we would expect only partial
crystallization/amorphization, while if the number is too large, the software
will keep raising the voltage beyond the device tolerance and would lead to
failure. Additionally, the geometry and performance requirements of the device
must be considered when deciding what the target reflectance delta should be.
For example, a device consisting of only the 2-D dot array pattern would
achieve a lower mean switching value than one consisting of only thin films
due to the lower areal fill factor of PCM.

The algorithm assesses changes in the reflectivity of the device based on the
user specified $\Delta R$ to decide whether to adjust voltage levels or
maintain them. Another important parameter in the implemented algorithm is the
healthy switchable area, which is defined as the area that consistently
exhibits reversible changes in reflectance during cycling. The areas that are
not showing any change in the reflectivity either have not been heated enough,
indicating a need for more voltage to switch, or have been damaged. It is very
important that the software detects damage before it propagates further and
decrease the voltage accordingly. For this, we monitor the number of pixels
that had already exhibited sufficient switching. Ideally, this number must
increase in the beginning of the cycling process and stays flat for the
reminder of the process before the damage occurs. For more details about the
algorithm implementation please refer to Supporting Note 02.

Figure 2: Comparing the pulse optimization process implemented by a human
operator and the adaptive algorithm on a PCM device. (a) An optical image of a
chip containing PCM device arrays mounted on and wire bonded to a custom
printed circuit board. The thin film, 1-D lines, and 2-D dot array PCM
patterns on a 150 $\upmu$m $\times$ 150 $\upmu$m microheater are shown on the
right; (b) A graph of the mean reflectance values of a PCM device, as well as
the pulse voltages applied during the experiment and simulated pulses
generated with the software while post-processing; (c) the conditions checked
by the software to update the input parameters to ensure equal switching; (d)
the condition checked by the software to update the input parameters to
increase the $\Delta$R; (e) the percentage area of the device that is
switching; (f), (g), and (h) the $\Delta$R between the crystalline and
amorphous phases at switching events 100, 150, and 250 respectively.

##  3 Results and Discussion

####

Our investigation begins with examining a microheater with dimensions of
150$\upmu$m $\times$ 150$\upmu$m that encompasses all three different PCM
pattern geometries as depicted in Fig. 2(a). Initially, we try to validate our
algorithm implemented in the software by conducting tests on this device,
where human operators make decisions on voltage adjustments. The results from
this experiment are crucial for optimizing the control logic within the GUI
and ensuring the computer can replicate human decisions accurately. To prevent
damage to the device, we initiate the experiment with a conservative voltage
level well below the thresholds for crystallization and amorphization. We
begin the crystallization pulses at 15 V and the amorphization pulses at 31 V,
gradually raising the pulse voltage until we detect reversible changes in the
sample’s reflectivity. To evaluate phase transformation, we capture an image
from the device before each pulse and take the average reflectivity of the
pixels within the region covered by PCM. This average reflectivity of the PCM-
covered area as a function of switching event is depicted in Figure 2(b),
along with the respective applied voltages. The inset provides a closer view
of the switching consistency between cycles, after the pulse voltage for
crystallization and amorphization reaches sufficient threshold to trigger
phase transformation. Note that the voltage is updated only after every 14
switching events.

At the beginning of the experiment, the amorphization and crystallization
pulses were not balanced, i.e. the applied pulses crystallize and amorphize
PCM to a different extent, as is apparent in switching events 1-100.
Specifically, the crystallization voltage proved insufficient to trigger
crystallization of the PCM, while the amorphization voltage gradually
increased the amorphized volume. Consequently, this leads to a decrease in
reflectance below the initial uncycled state. At this point, the operator
increases the crystalline voltage to trigger crystallization. The device then
stabilizes in an amorphous state and the crystallization begins to catch up.
Once the device stabilized again around the initial state, the operator
continues to observe any trends in either pulse that reduce the reflection
contrast ($\Delta$R) and increases the amplitude of that pulse accordingly.
Although the experiment was conducted by a human controller, the logic
outlined in section 2.3 was developed with the collected data. The ability of
the software to detect these trends in the reflectivity in each phase is shown
in Fig. 2(c). The green lines indicate trends that the logic detected as
lowering the $\Delta$R, while the red lines indicate that the trend was not
significant enough to warrant a change in the pulse. This shows a good
agreement between the decisions the human operator made and the software
performance in adjusting the voltage.

After stabilizing the switching around switching event 100, the operator
begins the process of increasing the $\Delta$R by increasing the amplitude of
both pulses. In order to ensure that the device is switching consistently,
this is only done when the mean value of each cycle is constant. Fig. 2(d)
shows a similar logic being implemented while post-processing the data. A
linear fit was applied to the mean reflectance values of sets of 28 switching
events. This longer duration was chosen to be more cautious and ensure that
the device was stable. The green lines indicate regions where this criterion
was satisfied and the red lines mark groups that did not meet this consistency
check. These evaluations showed good agreement with the decisions the human
operator had made during the experiment when trying to increase the $\Delta$R.
After running these switching criteria checks in the results post-processing
for this device and several other similar devices (refer to Supporting Note
03), these conditions proved to be a good representation of the input
parameter changes the human operator made. The computer-generated pulses are
shown in Fig. 2(b) and can be compared to the human-operated pulses. The
computer mimicked the human decisions at each 14th switching event closely and
erred on the side of caution when it deviated.

As the device was cycled through, the switched area of the heater grew as
well. The percentage area of the device switching as the experiment progressed
is shown in Fig. 2(e). The maximum value reached was around 50%. Once a large
$\Delta$R was achieved, the experiment was terminated after 300 switching
events. A pixel was assumed to be switching if it had a $\Delta$R of at least
100 counts to avoid capturing noise in the camera as a switched region. This
is a reasonable assumption considering that the color maps in Fig. 2(f-h)
where the areas with adequate contrast show a close representation of the
actual PCM patterns. Even with the small number of switching events, the phase
transformation behaviors of the three PCM patterns differ considerably. It can
be seen from Fig. 2(f-h) that although the change in mean reflectance for the
device increases upon cycling, the unpatterned PCM thin film regions on the
device begin to delaminate and the corresponding damage spreads to the other
areas of the device as the device was cycled further. However, the line and
dot array patterns showed much more resilience and were less prone to
delamination.

The investigation into the behavior of PCM devices revealed significant
differences in switching characteristics among various geometries. Notably,
larger PCM regions tend to require higher switching voltages, which can be
attributed to their larger thermal mass. This observation underscores the
challenge of achieving uniform switching across non-uniform PCM geometries,
which is particularly relevant for applications such as beam-focusing
metasurfaces where precise control over switching several different PCM
pattern geometries is crucial.

Once we verified the logic for controlling the applied pulses is operating as
expected, we moved to a different device to implement the adaptive pulse
optimization and evaluate the performance of the device for computer-based
control. For this, we use a larger microheater 200 $\upmu$m $\times$ 200
$\upmu$m, as is shown in Fig. 3(a). This device consisted of 40 $\upmu$m
$\times$ 40 $\upmu$m square thin film regions, 40 $\upmu$m $\times$ 2 $\upmu$m
1-D lines, and 2 $\upmu$m $\times$ 2 $\upmu$m 2-D dot arrays. The image in
Fig. 3(b) was taken after the cycling the device for 7000 switching events,
and shows the damage on the device due to delamination. Fig. 3(c) is provided
as a reference for the IR image of the device prior to cycling. Figs. 3(d-f)
demonstrate the formation of delamination areas in a central thin film region,
which propagates from the thin films to the surrounding regions. The footprint
of the damage increases towards the end of the experiment respectively.

Figure 3: PCM device switching performance using the adaptive pulse
optimization algorithm. (a) An optical image of a device taken before the
experiment featuring PCM thin film, 1-D line, and 2-D dot array geometries on
a 200 $\upmu$m $\times$ 200 $\upmu$m microheater; (b) an optical image of the
device taken after the experiment; (c) SWIR image of the device in its as-
deposited state taken prior to the start of the experiment; (d), (e) and (f),
the $\Delta$R between the crystalline and amorphous phases at switching events
50, 500, and 5000 switching events respectively; (g) the average reflectance
of the device as a function of switching event; (h) the area of the device
that is switching as a function of switching event; and (i) the switching
ratio of the pixel with the largest switching value.

This device was cycled for 7000 switching events, and the average device
reflectivity is over the course of the experiment is shown in Fig 3(g). The
$\Delta$R decreased as it was cycled past 500 switching events. This trend was
due to the reduction of the switching area, not a drop in the contrast in
areas where delamination did not occur and the PCM continued to switch. Fig
3(i) shows the percentage of the device’s area that was switching, and due to
the spread of the damage, the value steadily decreases. The $\Delta$R at the
healthy part of the device, shown in Fig. 3(i), maintains about 40% switching
for the full duration of the experiment. This indicates that if the
delamination damage spread could have been mitigated, it may have been
possible to sustain a large $\Delta$R for the full device.

Given that patterned PCM exhibits superior endurance compared to blanket PCM
films against delamination damage, we postulate that a device consisting
solely of PCM patterns will significantly outperform the previously discussed
devices. This is verified by our experimental results using devices that
contain exclusively 1-D line patterns as illustrated in Fig. 4(a), featuring
dimensions of 2$\upmu$m$\times$40$\upmu$m for the upper half and
3$\upmu$m$\times$40$\upmu$m for the lower half, onto a 200$\upmu$m $\times$
200$\upmu$m microheater. Figures 4(b-d) demonstrate the contrast between
images taken after crystallization and amorphization by the SWIR camera.
Essentially, these images represent the difference in reflectivity of the PCM
($\Delta R=|R_{crys}-R_{amor}|$) between the crystalline and amorphous phases
at switching events 125, 1250, and 12500. Brighter areas indicate a more
significant change in PCM reflectivity during the switching event, which
suggests a stronger phase transformation.

Figure 4: Demonstrating enhanced endurance in a device containing only 1-D
line pattern PCM geometries. (a) An image of a device featuring 2$\upmu$m
(upper half) and 3$\upmu$m (lower half) gratings on a 200$\upmu$m $\times$
200$\upmu$m microheater; (b-d) switching contrast images of the device
contrast at switching events 125, 1250, and 12500 respectively; (e) the mean
reflectance of the healthy PCM regions of the device as a function of
switching event; (f) the prescribed voltages and measured resistances during
the experiment; (g) the prescribed voltages and measured resistances at the
end of the experiment (h) the device area switching as a function of switching
event; (i) the ratio of the reflectivity in crystalline to amorphous phase for
the pixel with the maximum switching each switching event; and (j) an image of
the device taken at the conclusion of the cycling.

Similar to previous measurements, we begin the experiment with low voltage
pulses and gradually raise the voltage to detect changes in the reflectivity.
Figure 4(e) shows the mean reflectivity as a function of switching event for
the entire heater area. A video demonstrating the switching process and damage
formation is available in the supplementary materials. The corresponding pulse
voltages as well as the heater resistance is plotted in Figure 4(f). According
to our observations, the reflectivity of the PCM-covered area on the
microheater remains unchanged during the first few tens of cycles. However, as
the pulses reach 17 V for crystallization and 40 V for amorphization, we start
to detect minor shifts in the reflectivity of the lines as shown in 4(e). As
we increase the voltage further, we observe a larger contrast in reflectivity
between the amorphous and crystalline states. However, at switching event 399,
we notice that a small portion of the sample stops switching upon phase
transformation, and this region expands further during cycling, as depicted in
Fig. 4(c) at switching event 1250. As the device continues to be cycled, the
algorithm detects slight changes in the reflectance and adjusts the parameters
accordingly. These deviations were recognized by the computer after thousands
of cycles of consistent switching. Detecting these would be difficult for a
human, given their slow and lengthy nature, underscoring the utility of using
a computer-aided algorithm to optimize and maintain the switching of the
device. Towards the end of the experiment, the resistance decreased slightly
but then increased drastically, as shown in Fig. 4 (g). This drastic increase
in resistance is due to the damage to the heater.

Figure 4(h) shows the maximum switching area covered by the PCM as a function
of switching event. Initially, the switching area increases gradually and
peaks at approximately the 200th switching event, with over 90% of the device
area undergoing a phase transformation. Over the following 200 switching
events, the switching area remains consistent before signs of damage begin to
appear. As the damaged area enlarges, the maximum switching area decreases
until the 1000th switching event. Following this, it maintains a relatively
steady level until the device eventually fails. In Figure 4(i), the maximum
switching ratio ($R_{c}/R_{a}$) is depicted as a function of switching event.
Here, we focus on the most substantial contrast, irrespective of the switching
area, which could be as small as one pixel. This parameter signifies that in
an ideal scenario where we could achieve perfect PCM switching uniformly
across the entire microheater area, we would observe nearly a 50% change in
reflectance at the operating wavelength of the camera. Fig. 4(j) shows an
image of the device at the conclusion of the experiment. The damage in the
heater due to electromigration of the metal contact is evident in the
blackened regions visible on the right half of the device.

Throughout the cycling experiment, the GUI facilitated dynamic adjustments to
input voltages in response to changes in the desired reflectance delta,
ensuring optimal device performance. Of particular significance is the
observation of PCM exhibiting good switching performance towards the end of
the experiment, with a maximum optical contrast nearing 1000 counts. This
shows the benefits of managing the input parameters with the GUI to avoid
prematurely over-stressing the device due to human errors and impatience. This
experiment was repeated for another similar pad, and the results of that test
have been included in Supporting Note 04.

We can see that the patterned PCM, i.e. the lines and dot arrays, demonstrated
greater resistance to damage propagation compared to unpatterned thin films.
In previous assessments of PCM devices that were continuous films, damage
propagation occurred rapidly following initial delamination[39, 47, 18]. In
contrast, the patterned PCM demonstrated distinct damage mitigation patterns.
These findings emphasize the intricate interplay between PCM geometry,
switching behavior, and device durability. This improved durability may be
attributed to the presence of direct contact areas between the Al2O3/SiN
capping layer and the heater in patterned PCM regions. As a result of better
adhesion between the capping layer and Si heater compared to that between PCM
and heater, localized delamination in the patterned areas minimally affects
neighboring regions. This highlights the potential of these geometries for
applications requiring prolonged device lifetimes and reliable performance.

In conclusion, using a systematic approach, we developed a GUI to automate
measurements and control pulse amplitudes, carefully pushing the device to its
maximum contrast between phases while minimizing damage. Our results indicate
that blanket-coated PCM films are highly prone to delamination due to weak
adhesion between the PCM and heater, whereas patterned structures such as
lines and dot arrays exhibit greater resilience over repeated cycles. This is
attributed to the direct contact between the encapsulating layer and heater in
the patterned regions, which enhances robustness and effectively prevents the
propagation of damage. Notably, we achieved over 13,000 switching events for
PCM line patterns on a 200 $\upmu$m $\times$ 200 $\upmu$m microheater. This
underscores the benefit of using micro-structured PCM architectures such as
diffraction gratings and metasurfaces for device longevity, as it provides
locations where the encapsulating layer can directly bond to the heater
underneath, acting as pillars to inhibit damage spread. Our findings will
contribute to enhancing the reliability of electrically switchable PCM micro-
optical devices toward their practical deployment.

##  4 Disclaimer

Specific vendor and manufacturer names are explicitly mentioned only to
accurately describe the test hardware. The use of vendor and manufacturer
names does not imply an endorsement by the U.S. Government nor does it imply
that the specified equipment is the best available.

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## Data availability

The data that support the findings of this study are available from the
corresponding author upon reasonable request.

## Acknowledgements

The authors appreciate the support by Mr. Ronald Neale and Mr. Stanley H.
Husch in graphic design and Mr. William Humphreys, chief engineer at NASA
LaRC, for review with substantial feedback on the paper. Mr. Parth Garud
performed his work while an intern funded through the NASA Office of STEM
Engagement. Dr. Kiumars Aryana performed his work funded by the NASA
Postdoctoral Program (NPP) through Oak Ridge Associated Universities (ORAU).
This research was sponsored by the National Aeronautics and Space
Administration (NASA) through a contract with ORAU and by the National Science
Foundation under Awards 2132929 and 2225968. This work was carried out in part
through the use of MIT.nano’s facilities. The views and conclusions contained
in this document are those of the authors and should not be interpreted as
representing the official policies, either expressed or implied, of the
National Aeronautics and Space Administration (NASA) or the U.S. Government.
The U.S. Government is authorized to reproduce and distribute reprints for
Government purposes notwithstanding any copyright notation herein.