# Neuromodulated Patience for Robot and Self-Driving Vehicle Navigation

Jinwei Xing
Department of Cognitive Sciences
University of California, Irvine
Irvine, CA 92697-5100
<EMAIL_ADDRESS>
Xinyun Zou
Department of Computer Science
University of California, Irvine
Irvine, CA 92697-5100
<EMAIL_ADDRESS>
Jeffrey L. Krichmar
Department of Cognitive Sciences
Department of Computer Science
University of California, Irvine
Irvine, CA 92697-5100
<EMAIL_ADDRESS>

###### Abstract

Robots and self-driving vehicles face a number of challenges when navigating
through real environments. Successful navigation in dynamic environments
requires prioritizing subtasks and monitoring resources. Animals are under
similar constraints. It has been shown that the neuromodulator serotonin
regulates impulsiveness and patience in animals. In the present paper, we take
inspiration from the serotonergic system and apply it to the task of robot
navigation. In a set of outdoor experiments, we show how changing the level of
patience can affect the amount of time the robot will spend searching for a
desired location. To navigate GPS compromised environments, we introduce a
deep reinforcement learning paradigm in which the robot learns to follow
sidewalks. This may further regulate a tradeoff between a smooth long route
and a rough shorter route. Using patience as a parameter may be beneficial for
autonomous systems under time pressure.

_K_ eywords autonomous vehicles $\cdot$ deep reinforcement learning $\cdot$
impulsiveness $\cdot$ navigation $\cdot$ neuromodulation $\cdot$ road
following $\cdot$ serotonin

##  1 Introduction

Real-world environments can change due to the season, time of day,
construction, or the behavior of other agents. Uncertainty can arise due to
sensor noise, unforeseen obstacles or uncertain goals. An autonomous system
needs to cope with these challenges and have the ability to adapt its behavior
based on the current situation.

Successful behavior requires a tradeoff between patience and assertiveness.
For example, a self-driving car may get stuck at a four-way stop sign because
human drivers are not waiting their turn. Or an autonomous robot whose task is
to thoroughly explore an environment may follow this directive even if it runs
out of power. In both cases, a signal regulating the patience of the system
would be beneficial. The self-driving car would eventually assert itself, and
the navigating robot would quit its exploration and seek a charging station.

The brain has a number of neuromodulators that regulate context, signal
changes, and direct actions. The neuromodulator serotonin (5-HT) is thought to
have a role in harm aversion, anxious states, and temporal discounting [1, 2,
3]. One theory posits that serotonin regulates the impulsiveness of action.
Recently, Miyazaki and colleagues showed that optogenetically increasing
serotonin levels caused mice to be more patient, especially when the timing of
a reward was uncertain [4]. Based on these results, they developed Bayesian
decision model for the probability to wait or quit.

In the present paper, we apply this model of impulsiveness to robot
navigation. Specifically, the robot will navigate through a series of
waypoints. The level of serotonin dictates how patiently the robot will search
for a waypoint. We show that changing the serotonin level can have dramatic
effects on the robot’s behavior. Such a system may be beneficial for adjusting
autonomous behavior depending on the dynamics and uncertainty of the
environment.

##  2 Methods

###  2.1 Navigation task

A set of robot navigation tasks were carried out in two different parks.
Figure 1 shows satellite images of the two parks. The waypoints for each park
are superimposed on the maps. Waypoints were GPS coordinates placed on
sidewalks in the park. The park on the left of Figure 1, Encinitas Community
park, was relatively flat. The park on the right of Figure 1, Aldrich park at
the University of California, Irvine, was hilly with numerous obstacles. It
also should be noted that Aldrich park was in a sunken bowl surrounded by tall
university buildings. These features made GPS signals unreliable.

In both parks, the robot was to proceed to each waypoint in order. If the
robot became impatient, it would skip searching for the present waypoint and
randomly choose a future waypoint. However, the robot had to reach the last
waypoint for a trial to be complete.

Figure 1: Parks at which robot navigation experiments were carried out. The
left is an image of Encinitas Community park and the right is an image of
Aldrich park at the University of California, Irvine. The labels denote the
waypoints. Waypoints were approximately 50-60 meters apart. Imagery from
Google Maps, 2019.

###  2.2 Robot and Software Design

For the robot experiments, we used the Android-based robotic platform [5, 6,
7], a mobile ground robot constructed from off-the-shelf commodity parts and
controlled through an Android smartphone (see Figure 2). An IOIO-OTG
microcontroller communicated with an Android smartphone via a Bluetooth
connection and relayed motor commands to a separate motor controller for
steering the Dagu Wild Thumper 6-Wheel Drive All-Terrain chassis. Three
ultrasonic sensors, which were used for obstacle avoidance, were connected to
the robot through the IOIO-OTG. The software application was written in Java
using Android Studio and deployed on a Google Pixel XL smartphone. The
application utilized the phone’s built-in camera, accelerometer, gyroscope,
compass, and GPS for navigation.

Figure 2: Android based robot used for the experiments.

For waypoint navigation, a GPS location was queried using the Google Play
services location API. The bearing direction from the current GPS location of
the robot to a desired waypoint was calculated using the Android API function
bearingTo. A second value, the heading, was calculated by subtracting
declination of the robot’s location to the smartphone compass value, which was
relative to magnetic north. This resulted in an azimuth direction relative to
true north. The robot traveled forward and steered in attempt to minimize the
difference between the bearing and heading. The steering direction was
determined by deciding whether turning left or turning right would require the
least amount of steering to match the bearing and heading. The navigation
procedure continued until the distance between the robot’s location and the
current waypoint was less than 20 meters, at which point the next waypoint in
the list was selected.

###  2.3 Waypoint Navigation and Model of Impulsivity

The robot proceeded through a list of waypoints as described above. However,
if the robot became impatient, it skipped the present waypoint and randomly
chose a waypoint closer to the final destination.

The likelihood to skip a waypoint was based on the Bayesian Decision Model
given by [4]. Specifically, we calculated the probability to wait given the
time elapsed.

$p(wait|t)={\frac{1}{1+exp^{\beta 5HT(t)}}}$ (1)

where was equal to 50 and 5HT(t) was the likelihood of reaching the waypoint
at time $t$. The likelihood was calculated with a Normal cumulative
distribution function having a mean of 40 seconds and a standard deviation of
20 seconds. The likelihood function was multiplied by a scalar that
represented the probability of receiving a reward. As in [4], we assume that
increasing serotonin levels causes an overestimation of the prior probability.
Therefore, in our experiments low serotonin equated to a probability of a
reward of 0.50 and high serotonin equated to probability of a reward of 0.95
(see [4] for details). Figure 3 shows the resulting probability to wait,
$p(Wait|t)$, curves.

Figure 3: Probability of waiting function. Higher serotonin (5-HT) levels
shifted the curve to the right resulting in longer wait times.

The $p(Wait|t)$ curves in Figure 3 were used to decide whether to keep
searching for a waypoint, or forego the desired waypoint and choose another. A
random number between 0 and 1 was generated and if the number was greater than
$p(Wait|t)$, where $t$ is the time elapsed searching for a waypoint, the robot
stopped searching for this waypoint. A new waypoint was randomly chosen that
was closer to the final destination. Note that if the robot was searching for
the final destination waypoint or for a new waypoint after a skip, the
$p(Wait|t)$ curve was not referenced. That is, the robot had to reach the
shortcut waypoint and had to reach the final waypoint for a successful trial.

###  2.4 Road Following with Deep Reinforcement Learning

A road following algorithm was used in some of the experiments carried out in
Aldrich park. We used deep reinforcement learning models for online learning
of a driving policy on the Aldrich park sidewalks. Figure 4 shows the data
pipeline. The Android Based Robot took pictures with the smartphone’s camera.
Using a “hotspot”, the image was sent to a nearby laptop, which performed
image segmentation of “road” vs. “non-road”. The laptop had a deep
reinforcement learning network, based on DQN [8], which processed the image,
and suggested an action. The actions ranged from sharp left to slight left to
straight to slight right and to sharp right. The robot got a reward if it
stayed on the road and a penalty if it went off the road. The laptop took
about 400 ms to process the information, generate an action, and update the
network. This was adequate for online learning in real-time.

Figure 4: High level illustration of the data pipeline for the road following
algorithm. Images from the Android based robot’s smartphone camera are sent to
a nearby laptop via a socket. The laptop runs a deep reinforcement learning
algorithm, which rewards staying on the road, and generates steering actions
for the robot.

To provide road information to the network, we used a semantic segmentation
neural network [9]. This allowed us to rapidly label road and non-road
portions of a scene. The network was then trained to generalize this
information for scenes the robot viewed in Aldrich park. The robot learned to
follow the road after roughly 500 training trials. For now, we are segmenting
road and non-road. But, potentially, we could segment people, trees, benches,
etc. These object classes could be used as further inputs for training the
network.

A detailed illustration of the road following deep reinforcement neural
network is given in Figure 5.

Figure 5: Detailed illustration of the data pipeline for the road following
algorithm. Enet was used to segment road from non-road [9]. The network gave a
positive reward for actions that kept the robot on the road and a penalty for
actions that caused the robot to go off road. Training was based on a DQN
reinforcement learning paradigm [8]. Training and testing were carried out
online in Aldrich park.

##  3 Results

Two sets of robot navigation experiments were carried. One set was in the
Encinitas Community park (see Figure 1 left) and the other was in Aldrich park
(see Figure 1 right). In both cases, the robot navigated through a set of
waypoints with low and high serotonin levels. In Aldrich park, an additional
navigation experiment was carried out with road following.

###  3.1 Waypoint Navigation in Encinitas Community park

We ran 6 trials for low serotonin and 6 trials for high serotonin in the
Encinitas Community park (see Figure 6). The waypoints were roughly 50-60
meters apart. In Figure 6, each colored marker denotes a trial. The markers
denote the GPS location from the smartphone when the robot was within 20
meters of a waypoint. Note that this reading could vary dramatically due to
GPS inaccuracies.

The level of serotonin affected the robot’s patience in finding a waypoint.
Over the 6 trials, 9 waypoints were skipped when serotonin was low, and 2
waypoints were skipped when serotonin was high. The average time before
skipping a waypoint was 68 seconds for low serotonin and 97 seconds for high
serotonin. These experiments demonstrated how this model could change route
planning behaviors.

Figure 6: Robot navigation trials in the Encinitas Community park. The black
dots are the waypoint destinations (WP1 – WP10). There were 6 trials. Each
color represents an individual trial. Each colored marker denotes the robot
reaching a waypoint.

Figure 7 shows the GPS readings from two trials, one with high serotonin and
the other with low serotonin. In the high serotonin trial, the robot reached
every waypoint. In the low serotonin trial, the probability to wait was
exceeded for reaching waypoint 6 after 69 seconds and the robot skipped to
waypoint 9. A video of the robot performing waypoint navigation with low
serotonin can be found at: https://youtu.be/6EcNchTGLKw and a video of the
robot performing waypoint navigation with high serotonin can be found at:
https://youtu.be/q_m0gbVN6UE

Figure 7: Two representative navigation trials in the Encinitas Community
park. All the GPS points are shown. The markers in the figure correspond to
the grey markers in Figure 6.

###  3.2 Waypoint Navigation in Aldrich park

We ran four high serotonin trials in Aldrich park on the University of
California, Irvine campus (see Figure 8). Since the area is sunken in a bowl
surrounded by tall buildings and trees, the GPS readings can be highly
inaccurate. In particular, the robot had difficulty finding waypoints 2 and 3.
On all four trials, after around 98 seconds trying to find a waypoint, the
robot took shortcuts to a later waypoint.

Figure 8: Four trials in Aldrich park with high serotonin. GPS readings are
shown with a colored marker. Different colors denote different trials.

In an additional trial, we introduced the road following algorithm (see Figure
9). In this trial, the robot stayed on the sidewalk and was able to reach
every waypoint within the probability to wait constraint. It should be noted
that during road following, the robot took longer to complete the course
(6:40) than when shortcuts were taken (6:01 average). However, the robot
reached more waypoints and took less energy, since it was traveling over
smoother terrain, than the trials when it took shortcuts. These results show
the benefit of adding additional navigation tools and the tradeoffs associated
with being patient versus being impulsive. A video of the robot navigating
using road following can be found at: https://youtu.be/DixOxO2UafQ

Figure 9: Android based robot using the road following algorithm.

##  4 Discussion

In the present paper, we showed how a concept from behavioral neuroscience
could be applied to robot navigation and possibly self-driving vehicles. It
has been shown that serotonin in the brain affects impulsiveness in an
animal’s behavior [4, 10]. The present model applied this idea to waypoint
navigation in autonomous robots. Specifically, we showed that simulating high
serotonin led to increased search time for a desired location and that
simulating low serotonin led to an increase in calling off the search for some
waypoints. Even under high serotonin conditions, if a waypoint was
particularly difficult to find or there were environmental challenges, there
is a limit to how long the robot will try to reach a desired location (see
Figure 8).

The goal of the present algorithm and demonstrations was not to achieve some
benchmark, but rather to suggest a neurobiologically inspired strategy that
could complement other navigation systems. The present approach could be
applied to biomimetic navigation systems [11, 12], as well as engineering
approaches to navigation [13, 14]. In general, the probability to wait
suggests a level of urgency in the overall system. We imagine this could be
applied to a number of tasks where resource allocation is time critical.

Furthermore, the probability of waiting could be associated to some internal
parameter in the system (e.g., battery level or prioritizing goals).
Presumably, the impulsiveness signal in the rodent is closely tied to its
natural foraging behavior. The animal will search for food, but the time it
will search depends on the food value and on the uncertainty of the food
resource. Such considerations could be beneficial for a robot navigation
system or for a self-driving vehicle.

The road following algorithm adds another dimension to the present navigation
system. By giving the robot an alternative to point-to-point navigation, the
robot now must weigh the cost of staying on a smooth and reliable road that
may take longer versus traversing over a rough terrain that may be shorter but
takes more energy and may be harmful to the robot. Since the deep
reinforcement learning introduced here is designed for online learning, these
costs could be learned along with the rewards for staying on the road.
Ideally, the deep reinforcement learning algorithm could set the serotonin
level dynamically.

The present algorithm is a step towards a complete navigation or self-driving
system that takes inspiration from neurobiology and behavioral neuroscience.

##  5 Acknowledgements

This work was supported by the Defense Advanced Research Projects Agency
(DARPA) via Air Force Research Laboratory (AFRL) Contract No. FA8750-18-C-0103
(Lifelong Learning Machines: L2M), and by the Air Force Office of Scientific
Research (AFOSR) Contract No. FA9550-19-1-0306. The authors would like to
thank Nicholas Ketz, Soheil Kolouri and Andrea Soltoggio for fruitful
discussions and suggestions. The authors would also like to thank Katsuhiko
Miyazaki for making his code available.

## References

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  * [2] Michael C. Avery and Jeffrey L. Krichmar.  Neuromodulatory systems and their interactions: A review of models, theories, and experiments.  Frontiers in Neural Circuits, 11(108), 2017.
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# Illuminating trap density trends in amorphous oxide semiconductors with
ultrabroadband photoconduction

George W. Mattson1, Kyle T. Vogt1, John F. Wager2 and Matt W. Graham1
Department of Physics, Oregon State University, Corvallis, OR 97331-6507, USA
School of EECS, Oregon State University, Corvallis, OR 97331-5501, USA

###### Abstract

Under varying growth and device processing conditions, ultrabroadband
photoconduction (UBPC) reveals strongly evolving trends in the defect density
of states (DoS) for amorphous oxide semiconductor thin-film transistors
(TFTs). Spanning the wide bandgap of amorphous InGaZnOx (a-IGZO), UBPC
identifies seven oxygen deep donor vacancy peaks that are independently
confirmed by energetically matching to photoluminescence emission peaks. The
sub-gap DoS from 15 different types of a-IGZO TFTs all yield similar DoS,
except only back-channel etch TFTs can have a deep acceptor peak seen at 2.2
eV below the conduction band mobility edge. This deep acceptor is likely a
zinc vacancy, evidenced by trap density which becomes 5-6$\times$ larger when
TFT wet-etch methods are employed. Certain DoS peaks are strongly enhanced for
TFTs with active channel processing damage caused from plasma exposure. While
Ar implantation and He plasma processing damage are similar, Ar plasma yields
more disorder showing a $\sim 2\times$ larger valence-band Urbach energy, and
two orders of magnitude increase in the deep oxygen vacancy trap density.
Changing the growth conditions of a-IGZO also impacts the DoS, with zinc-rich
TFTs showing much poorer electrical performance compared to 1:1:1 molar ratio
a-IGZO TFTs owing to the former having a $\sim$10$\times$ larger oxygen
vacancy trap density. Finally, hydrogen is found to behave as a donor in
amorphous indium tin gallium zinc oxide TFTs.

amorphous IGZO, thin-film transistor, density of states, ultrabroadband
photoconduction

##  I Introduction

Amorphous oxide semiconductors (AOS) such as amorphous indium gallium zinc
oxide (a-IGZO) have achieved widespread adoption as the active channel
material in optical display thin-film transistors (TFTs) because of their high
mobility, low processing cost, and high on-off current ratio.[1, 2] a-IGZO is
a wide bandgap semiconductor material with $\mathrm{E_{g}}$ ranging from 3.1
to 3.5 eV. Owing to its amorphous structure, a-IGZO has a large concentration
of sub-gap vacancy sites that serve as the dominant electron donation
mechanism for its n-type TFT operation.[3, 1] The subgap states in a-IGZO also
act as electron traps that impact device performance by introducing transfer
curve hysteresis and bias illumination stressing.[4, 5, 6, 7, 8, 9]
Fabrication processes for AOS TFTs have a marked effect on the overall
characteristics of the resulting devices, which is reflected in the
composition of the subgap states.

Energetically, these trap states span different ranges of the subgap depending
on their local charge environment and consequent trapping behavior. Multiple
configurations of donor-like oxygen vacancies ($\mathrm{V_{O}}$) dominate the
shallow and midgap regions.[10, 11, 12, 13] Additionally, both acceptor-like
zinc vacancies ($\mathrm{V_{Zn}}$)[6] and an OH-related state
($\mathrm{{[{O_{O}^{2-}}{H^{+}}]}^{1-}}$)[14] have been documented; these
states are convolved with an exponentially decaying Urbach[15] tail reflecting
O 2p disorder in the amorphous matrix.[16]

The donor-like $\mathrm{V_{O}}$ states have received considerable attention
from researchers over the years.[10, 11, 12, 13] $\mathrm{V_{O}}$ are the
dominant donor-like trap state in AOS materials. In a-IGZO, this electron
donation pins the Fermi energy ($\mathrm{E_{F}}$) near the CBM, making them
n-type semiconductors. While electron donation from thermally depopulated
shallow donor $\mathrm{V_{O}}$ states typically contributes the n-type
carriers necessary for AOS enhancement-mode operation, $\mathrm{V_{O}}$ states
have been variously linked to effects such as hysteresis[17] and persistent
photocurrents[18]. Process tuning of $\mathrm{V_{O}}$ trap states to amplify
AOS TFT electronic performance while avoiding deleterious stability or leakage
phenomena remains an ongoing research challenge.

This work reveals trends in how the subgap density of states (DoS) in AOS TFTs
evolves over different processing methods, TFT architectures, and channel
compositions. In section III, we employ the ultrabroadband photoconduction
(UBPC) method to reveal systematic trends in subgap DoS. Section III is broken
down into five sub-sections that explore the DoS of AOS materials in the
context of: (A) Comparison ultrabraodband optical vs. UBPC photoconductive
methods of defect state identification, (B) etch process-induced vacancy state
formation in back-channel etch vs. top-gate a-IGZO TFTs, (C) plasma and ion
implantation treated a-IGZO top-gate TFTs, (D) a-IGZO TFTs with non-
stoichiometric active channel compositions, and (E) hydrogen incorporation
into amorphous indium tin gallium zinc oxide (a-ITGZO). Below, we summarize
the motivation for each study.

Section III.A compares the subgap defect peaks identified by UBPC in AOS TFTs
by matching peaks obtained to more conventional optical methods, including
photoluminescence (PL). AOS TFT device electrical metrics have improved over
the last two decades. The optical response at subgap defect energies in AOS
materials is often $\mathrm{\sim 10^{6}}$ smaller than at the bandgap. While
x-ray or ultraviolet photoelectron spectroscopy (XPS, UPS) have become popular
tools for AOS characterization, they are not capable of achieving the
necessary resolution to reliably observe the subgap trap states.[19, 20] The
recently developed UBPC method has the potential to be a standard experimental
technique to quantify the subgap trap state density analytically.

Section III.B statistically compares the DoS trends for back channel etch
(BCE) vs. top-gate (TG) a-IGZO TFTs. Fabrication of BCE AOS TFTs is convenient
for TFT arrays in display backplane applications.[21] However, multiple
researchers have observed that the etch process traditionally employed in
fabricating BCE a-IGZO TFTs results in device degradation,[21] ostensibly due
to the creation of metal vacancy states which are introduced via the
etchant.[22, 23, 24]

Section III.C compares the change in DoS of a-IGZO TFTs after plasma and ion
implantation methods are applied to the full active channel. Extensive prior
research has been performed on plasma[25, 26, 27, 28, 29, 30, 31, 32, 33, 34]
and ion implantation[35] treatment of a-IGZO TFTs. Such plasma treatment have
a variety of purposes ranging from defect creation[25, 31] to defect
passivation[26, 36, 37] to use as a dry etchant for the source-drain electrode
metal.[38, 39] In a-IGZO, Such plasma methods commonly increase the TFT
conductivity up to $\mathrm{\sim 50X}$which is desirable for improving
electrical contacts.[25, 27, 31] After plasma treatments, the UBPC method will
be used to understand how oxygen vacancy deep donor trap density is correlated
with conductivity enhancement. The plasmas selected for these treatments vary
greatly depending on the intended application: oxygen[27, 36] plasmas can
passivate $\mathrm{V_{O}}$ defects, while other plasmas can dope the device CB
via either hydrogen[26, 37, 34] incorporation or the formation of
$\mathrm{V_{O}}$ states (Ar, He[31] plasmas); mixed Ar-O[28, 29, 33] plasmas
have also been explored.

In section III.D, AOS growth conditions are changed by comparing the DoS of a
zinc-rich growth to that of ‘111’ (1:1:1 molar %
$\mathrm{In_{2}O_{3}/Ga_{2}O_{3}/ZnO}$ ratio) a-IGZO. Exploring non-
stoichiometric compositions of a-IGZO (with greater or lesser relative
concentrations of the In, Ga, and/or Zn metal constituents compared to 111
a-IGZO) is an active area of research for improving upon a-IGZO TFT
characteristics such as field effect mobility.[40, 41, 42, 43, 44, 45] These
efforts are sometimes linked to the deposition of bilayer[46, 47, 48, 49, 50]
(or trilayer)[51, 52, 53] active channels. These bilayer TFTs often contain a
stoichiometric or near-stoichiometric stability layer[43] with a boost layer
that is richer in certain constituent elements such as In[45, 49], doped with
metals such as Ti[54], or composed of a conductive material such as ITO[55])
intended to enhance the TFT electrical performance.

Lastly, Section III.E discusses hydrogen incorporation into indium tin gallium
zinc oxide (a-ITGZO) TFTs. a-ITZGO has been studied in recent years to reduce
the reliance of AOS TFTs on the scarce indium constituent without the adverse
device mobility effects that might be expected from reducing or eliminating
the molar proportion of indium.[56, 57, 58] While a-ITGZO TFTs exhibit
promising carrier mobility metrics, they also exhibit high susceptibility to
positive bias stressing (PBS)-related degradation effects attributed to subgap
trap states.[57] Hydrogen can be an abundant defect in AOS materials [59], but
the electrically active nature of this large interstitial defect is more
controversial. Recent work suggests this state that results from its
interaction with oxygen states located near the valence band mobility (VBM)
edge.[14] Although hydrogen has a donor effect on a-IGZO, its negative-U
nature results in a hydrogen electron donation that precedes its incorporation
into a-IGZO. [60, 61, 14] Specifically for a-IGZO TFTs, the hydrogen complex
state $\mathrm{{[{O_{O}^{2-}}{H^{+}}]}^{1-}}$ has been observed centered
$\mathrm{\sim 0.4}$ eV below the VBM edge.[16, 14] Section III.E extends prior
hydrogen studies to explore other AOS TFT materials.

##  II Experimental Methods

###  II.1 Amorphous oxide TFT device characterization

Trends in the subgap DoS are observed for a diverse selection of AOS back
channel etch (BCE) and top-gate a-IGZO TFTs discussed in each sub-section.
ITO/a-IGZO co-sputtered (a-ITGZO) TFTs possessing different hydrogen
concentrations are also fabricated. The TFTs possessing different hydrogen
concentrations were synthesized by subjecting the devices to varying annealing
time intervals to cause hydrogen migration from an H-rich $\mathrm{SiN_{y}}$
passivation layer adjacent to the active channel.[14]

Figure 1:  (a) The UBPC method illustrated uses tunable lasers and an all-
reflective 4f confocal scanning microscope to measure the DoS for the AOS TFTs
to within 0.3 eV of the CB mobility edge. (b) Shown for $h\nu=1.5$ eV, UBPC
microscopy spatially-resolves the TFT channel length PC-response,
$\mathrm{I_{PC}}$. Gate voltages resolve ’turn-on’ change from depletion-mode
($\mathrm{V_{G}<0}$ V) to enhancement-mode ($\mathrm{V_{G}>0}$ V) operation.
(c) Scanning-PC microscopy spatial maps corresponding to (red arrow) linecuts
shown in b with back-reflection map of a BCE a-IGZO TFT on right.

The photoluminescence (PL) emission spectra of a-IGZO thin films and devices
were taken using a Horiba Nanolog flourimeter using using photomultiplier
detection for the visible range, and liquid nitrogen-cooled InGaAs detection
over the 0.7 to 1.5 eV range. All PL data shown uses a 3.8 eV excitation
source. PL spectral shape on active TFTs was confirmed using an Ocean Optics
spectrometer under diffraction-limited illumination. The corresponding
ultrabroadand absorption spectrum for Tauc bandgap analysis was taken using a
Cary UV-VIS-IR spectrometer.

###  II.2 Sub-gap DoS by Ultrabroadband Photoconduction (UBPC) microscopy

In this work, the on-chip spectroscopic technique called ultrabroadband
photocoduction (UBPC)[6] is employed to obtain the experimental subgap trap
density and DoS for AOS TFTs. The essential elements of the UBPC setup are
depicted in Fig. 1(a). A laser source tunable over the a-IGZO subgap energy
range is focused onto the TFT active channel using a piezo scanning mirror
within a 4-f confocal scanning geometry which couples the source into an
Olympus BX61W microscope; all-reflective optics are employed throughout the
line to reduce spectral aberrations. The laser source was a tunable
Ti:Sapphire laser system (Coherent Chameleon Ultra II) coupled to an APE
Compact optical parametric oscillator, which provides a spectral range from
$0.2$ to $3.7$ eV, enabling probing of bot near-conduction and near-valence
band tail states. A homebuilt difference frequency generation line was
employed for probing of near conduction band states reported in Section III.E
to acieve laser energies below 0.2 eV. Select measurements were also verified
using a Fianium supercontinuum white light laser source coupled to a laser
line tunable filter (Photon Etc.), which yields high-throughput
characterization over the $0.7$ to $3.1$ eV range. Both setups are designed to
maintain Poynting vector illumination stability on the TFT during the spectral
scan.

A 52X cassegrain reflective objective is used to focus the laser onto a
diffraction-limited spot centered on the TFT. The a-IGZO TFT active channel is
operated under a forward bias of $\mathrm{V_{ON}+5}$ V; the TFT is
electrically connected to the measurement interface via a homebuilt electrical
probe setup consisting of RF source-drain electrical probes. At each
illumination wavelength, the photoconduction (PC) signal is retrieved from the
noise using a current pre-amplifier and a lock-in amplifier (Zurich HFLI). To
eliminate the contribution of dark current background and hysteretic drift, we
modulate the illumination source using an optical chopper at a frequency of
$585$ Hz and reference the lock-in amplifier to this frequency. Simultaneous
scanning photoconduction and back reflection maps are taken at each
illumination energy using a 4f confocal scanning geometry to ensure uniformity
of the illumination spot over the energy measurement range and across
successive measurements.

Figure 1(c) shows scanning back reflection (left) and scanning photoconduction
maps (SPCM, all other panels) for a back channel etch a-IGZO TFT upon
photoexcitation at $h\nu=$1.8 eV. The PC images are colorized via a heatmap
corresponding to the relative amplitude of the integrated trap density that is
optically excited. Line cuts as a function of gate voltages ($\mathrm{V_{G}}$)
ranging from depletion-mode to enhancement-mode operation conditions are shown
in Fig. 1(c). As the TFT gate voltage is scanned from depletion-mode
($\mathrm{V_{G}<0}$ V) to enhancement-mode ($\mathrm{V_{G}>0}$ V) operation,
the photoconduction amplitude becomes order of magnitude larger and becomes
uniformly distributed across the active channel length. In depletion-mode
operation ($\mathrm{V_{G}=-4}$ V), the device PC response is localized to the
immediate area of the drain electrode, indicative of a Schottky-like barrier
at the interface. The amplitude of the spatially-mapped UPBC response can be
directly mapped to generate the sub-gap DoS by spectrally scanning the energy
of the power-normalized excitation laser from 0.3 to 4 eV.

The photoconduction signal at each given photon energy ($\mathrm{h\nu}$) is
directly proportional to the total integrated subgap trap density.
Specifically, the integrated trap density $\mathrm{N_{TOT}({h\nu})}$ between
the conduction band mobility edge and $h\nu$ is given by:

$\mathrm{N_{TOT}(h\nu)}=\left(\mathrm{\frac{\mathrm{qN_{o}}C_{I}}{tm}}\right)\mathrm{\frac{I_{PC}}{N_{ph}}}$
(1)

where $\mathrm{I_{PC}}$/$\mathrm{N_{ph}}$ represents the PC signal
($\mathrm{I_{PC}}$) which has been photon normalized via through-chip laser
power correction at each photon energy, $\mathrm{h\nu}$. The bracketed
quantity is a scaling constant composed of the the electronic charge (q), the
gate insulator capacitance density($\mathrm{C_{I}}$), the slope (m) of the
non-illuminated $\mathrm{I_{D}-V_{G}}$ transfer curve within $\mathrm{\pm
0.5}$ V of the constant $\mathrm{V_{ON}+5}$ V forward operating gate bias
(taken immediately after each PC measurement), and the accumulation layer
thickness ($\mathrm{t}$). Finally, $\mathrm{N_{o}}$ is a constant calibration
term obtained by finding the saturation photon flux, corresponding to the
maximum photon flux that yields a detectable change in the in the PC-signal
detected for illumination just below the VB mobility edge. After directly
mapping the total integrated trap density as a function of photon energy from
the raw observable PC signal, we take its derivative to obtain the
experimental density of states (DoS):
$\mathrm{DoS({h\nu})}=\frac{d\mathrm{N_{TOT}}}{d\mathrm{({h\nu}})}$.
Throughout this work, we refer to experiment subgap DoS$\mathrm{(E-E_{C})}$
which is equivalent to DoS$\mathrm{(-h\nu)}$.

##  III Results and Analysis

###  III.1 a-IGZO DoS by UBPC vs. ultrabroadband optical methods

---
Figure 2:  Ultrabroadband PL emission and photoconduction spectra, both
correlated to a-IGZO subgap density of states peaks. (a) Tauc absorption
(black) and photoluminescence (PL, red) emission spectra. (Inset) PL emission
is due to excited conduction band electrons recombining into deep trap states.
(b) a-IGZO thin film PL emission spectrum (red) is plotted on the same axis as
the energy derivative of photoconduction (PC) spectrum of a back channel etch
(BCE) a-IGZO TFT. Dotted lines highlight correspondence between PL and UBPC
peaks. (c) UBPC density of states for a BCE a-IGZO TFT.

The predictive trends the UBPC DoS method is independently verified by
overlaying prominent DoS peaks with the a-IGZO photoluminescence emission
spectrum collected over an ultrabroadband spectral range of 0.7 to 4 eV.
Figure 2(a) plots Tauc[62] absorption $\mathrm{({\alpha\nu}^{1/2})}$ and
photoluminescence (PL) emission spectra. The estimated optical bandgap
($\mathrm{E_{g}}$) for a-IGZO from the Tauc plot is $\mathrm{3.17\pm 0.03}$
eV. PL reveals two strong subgap peaks at $\mathrm{\sim-1.4}$ and
$\mathrm{\sim-1.7}$ eV, and two weak subgap peaks at $\mathrm{\sim-0.8}$ and
$\mathrm{\sim-1.1}$ eV. The apparent drop-off in emission at
$\mathrm{\sim-3.5}$ eV is actually an artifact due to PL drop off associated
with use of a cut-off filter used to eliminate higher-harmonics in the
monochromator.

The four subgap PL peaks (red) of Fig. 2(a), as obtained from a 100 nm a-IGZO
thin film, are plotted on an expanded energy scale in Fig. 2(b) and are shown
to be correlated to photoconduction (PC) derivative ($\mathrm{\mu
AW^{-1}eV^{-1}}$) spectrum peaks (black), as obtained from a BCE a-IGZO TFT.
As evident from Fig. 2(b), the match between PL and PC derivative peaks is
quite striking. Three other PC derivative spectral features are indicated in
Fig. 2(b) at $\mathrm{\sim-0.3}$, $\mathrm{\sim-2.2}$, and $\mathrm{\sim-2.8}$
eV.

Atomic identification of the PL and PC derivative peaks shown in Fig. 2(b) is
shown in Fig. 2(c) and summarized in Table I. In Fig. 2(c), raw UBPC data
(black closed circles) is simulated (black solid line) by convolving a series
of Gaussian subgap defect peaks and an exponential valence band Urbach[15]
tail. The UBPC spectrum indicated in Fig. 2(c) corresponds to the BCE a-IGZO
TFT density of states parameters collected in Table I. UBPC density of states
parameters for a top-gate (TG) a-IGZO TFT are also included in Table I, and
are expected to be representative of the 100 nm a-IGZO thin film used for PL
assessment, as both a-IGZO films are prepared in a similar manner.

Several aspects of Fig. 2(c) are notable. First, the four PL peaks (red) of
Fig. 2(b) are ascribed to oxygen vacancies, O-2, O-3, O-4, and O-5 (see Table
I). Second, the strong peak occurring at -2.2 eV in both the PC derivative and
UBPC spectra is attributed to a zinc vacancy acceptor, Zn-8. A zinc vacancy
has previously been identified as the only cation vacancy energetically
favored to exist in a-IGZO.[6] Also, recently reported electron spin resonance
measurements confirm the existence of a zinc vacancy in a-IGZO at
concentrations up to about an order of magnitude greater than that of
estimated peak oxygen vacancy concentrations.[63] Note the absence of a -2.2
eV zinc vacancy peak in the PL spectrum of Fig. 2(b), consistent with the low
concentration of this peak in Table I for a TG a-IGZO TFT. Third, the PC
derivative and UBPC spectra show evidence of the O-1 oxygen vacancy peak at
-0.3 eV. Fourth, the PC derivative curve shows evidence of an
$\mathrm{{[{O_{O}^{2-}}{H^{+}}]}^{1-}}$ (or $\mathrm{OH^{-}}$) defect complex
state at -2.8 eV, while this state is not observed in the UBPC spectrum,
presumably because it is obscured by the valence band Urbach band tail.
Simulation of the UBPC spectrum reveals a valence band Urbach energy,
$\mathrm{E_{U}=110}$ meV.

---
Figure 3:  Density of states (DoS) trends for a top-gate (TG) vs. a back channel etch (BCE) a-IGZO TFT. (a) (Upper) UBPC comparison of a TG and a BCE a-IGZO TFT. (Lower) Simulated Gaussian subgap state peaks corresponding to the UBPC experimental data. (b) Linear DoS scaling highlights the Zn-8 zinc vacancy peak (red) for different BCE processing conditions: 1: Mo S/D wet etch, 2: Ti/Cu S/D wet etch, 3: Ti/Cu S/D wet/dry etch, and 4: Ti/Cu S/D low damage etch. (c) Drain current-gate voltage ($\mathrm{I_{D}-V_{G}}$) transfer curves for different BCE processing conditions. Defect | Peak Energy (eV) |  | Peak DoS ($\mathrm{\times 10^{15}cm^{-3}}\mathrm{eV^{-1}}$) |  | FWHM (meV) |
---|---|---|---|---|---|---
| BCE | TG | BCE | TG | BCE | TG
O-1 | $\mathrm{-0.3}$ | $\mathrm{-0.3}$ | $\mathrm{2.5}$ | $\mathrm{1.3}$ | $\mathrm{80}$ | $\mathrm{80}$
O-2 | $\mathrm{-0.85}$ | $\mathrm{-0.75}$ | $\mathrm{11}$ | $\mathrm{8.8}$ | $\mathrm{70}$ | $\mathrm{70}$
O-3 | $\mathrm{-1.2}$ | $\mathrm{-1.2}$ | $\mathrm{24}$ | $\mathrm{9.2}$ | $\mathrm{80}$ | $\mathrm{80}$
O-4 | $\mathrm{-1.35}$ | $\mathrm{-1.45}$ | $\mathrm{37}$ | $\mathrm{30}$ | $\mathrm{110}$ | $\mathrm{80}$
O-5 | $\mathrm{-1.8}$ | $\mathrm{-1.8}$ | $\mathrm{23}$ | $\mathrm{27}$ | $\mathrm{70}$ | $\mathrm{70}$
O-6 | $\mathrm{-2.0}$ | $\mathrm{-2.0}$ | $\mathrm{4}$ | $\mathrm{12}$ | $\mathrm{70}$ | $\mathrm{70}$
O-7 | $\mathrm{-2.4}$ | $\mathrm{-2.4}$ | $\mathrm{50}$ | $\mathrm{67}$ | $\mathrm{50}$ | $\mathrm{80}$
Zn-8 | $\mathrm{-2.2}$ | $\mathrm{-2.2}$ | 155 | 0.1 | $\mathrm{130}$ | $\mathrm{130}$
$\mathrm{{[{O_{O}^{2-}}{H^{+}}]}^{1-}}$ | $\mathrm{-2.8}$ | $\mathrm{-2.8}$ | $\mathrm{20}$ | $\mathrm{40}$ | $\mathrm{320}$ | $\mathrm{320}$
Table 1:  Extracted parameters from UBPC measurements on the BCE and TG a-IGZO
TFTs plotted in Fig. 3a show sub-gap defect peak energy ($\mathrm{E-E_{C}}$),
peak density of states (DoS), and peak full-width-half-maximum (FWHM) are
similar with the exception of the Zn-8 deep acceptor row (in bold).

###  III.2 Top-gate (TG) vs. Back Channel Etch (BCE) a-IGZO TFT Trends

Figure 3(a, upper) compares UBPC trends (circles: experimental data, solid
curves: simulation) for a TG a-IGZO TFT (blue, solid circles) to that of a BCE
a-IGZO TFT (green, solid circles). Figure 3(a, lower) plots the UBPC
experimental data of a TG a-IGZO TFT ($\mathrm{solid}$ circles) with a
simulation of the UBPC spectra based on Gaussian peaks convolved with an
exponentially decaying valence band Urbach tail. Simulated subgap peaks are
enumerated according to species ($\mathrm{V_{O}}$ donor, $\mathrm{V_{Zn}}$
acceptor, or $\mathrm{{[{O_{O}^{2-}}{H^{+}}]}^{1-}}$ donor) and by peak
energetic location (from CB $\mathrm{\sim 0}$ eV to VB $\mathrm{\sim-3.2}$
eV). The oxygen vacancy (blue) and $\mathrm{{[{O_{O}^{2-}}{H^{+}}]}^{1-}}$
(orange) simulated peaks plotted in Fig. 3(a) correspond to simulation fitted
to a TG UBPC spectrum, while the $\mathrm{V_{Zn}}$ peak (red) corresponds to
simulation fitted to a BCE UBPC spectrum. The characteristic energy of the VB
tail exponential decay (or Urbach energy) of the UBPC spectrum is derived from
the simulation of the experimental data by fixing all Gaussian subgap state
peak energies and amplitudes, leaving the Urbach energy (TG: 89 meV, BCE: 95
meV) as the only free simulation parameter. The fitted Gaussian peak
characteristics for the enumerated peaks are provided in Table I.

In Fig. 3(b), the UBPC DoS in the region of the Zn-8 zinc vacancy peak is
compared on a linear ordinate scale for several different BCE processing
conditions: 1: Ti/Cu S/D wet etch; 2: Mo S/D wet etch; 3: Ti/Cu S/D wet/dry
etch (wet etch of the S/D metal far from the back channel surface, followed by
dry etch near the back channel surface); and 4: Ti/Cu S/D low damage etch.
Figure 3(c) shows the drain current-gate voltage ($\mathrm{I_{D}-V_{G}}$)
transfer curves for three of the TFTs plotted in Fig. 3(b), as well as for a
TG a-IGZO TFT.

As clearly evident from Figs. 3(a) and 3(b), the Zn-8 zinc vacancy
concentration is strongly affected by the type of BCE etchant used. Also, a
larger Zn-8 zinc vacancy concentration leads to a more positive shift in the
turn-on voltage. From a charge balance perspective, an increase in the deep
acceptor concentration, i.e., Zn-8 zinc vacancies, is balanced by an increase
in the concentration of ionized deep donors, i.e., oxygen vacancies, e.g.,
O-1, O-2, O-3, etc. Mathematically, $\mathrm{\Delta N_{DA}\approx\Delta
N_{DA}(E_{F})}$, where the deep donor Fermi level dependence recognizes that
the Fermi level is modulated deeper into the bandgap, away from the conduction
band mobility edge, until a sufficient density of oxygen vacancy donors is
ionized in order to achieve charge neutrality.

The Zn-8 zinc vacancy concentration can be estimated by integration of its
UBPC peak area or alternatively from the shift in the turn-on voltage of an
$\mathrm{I_{D}-V_{G}}$ transfer curve via $\mathrm{N_{DA,ID-
VG}\approx(C_{I}\Delta V_{ON}/q)^{3/2}}$, where $\mathrm{C_{I}}$ is the gate
insulator capacitance density (9.7 $\mathrm{nF/{cm^{-2}}}$), q is the
electronic charge, and $\mathrm{\Delta V_{ON}=\Delta V_{ON,BCE}-\Delta
V_{ON,TG}}$.

BCE | Etchant |  $\mathrm{\Delta V_{ON}}$ (V) | $\mathrm{N_{DA,ID-VG}}$ |  $\mathrm{N_{DA,UBPC}}$ ($\mathrm{cm^{-3}}$)
---|---|---|---|---
#1: | wet | 2.55 | $\mathrm{6.1\times 10^{16}}$ | $\mathrm{6.2\times 10^{16}}$
#3: | wet/dry | 1.35 | $\mathrm{2.3\times 10^{16}}$ | $\mathrm{2.4\times 10^{16}}$
#4: | low damage | 0.50 | $\mathrm{5.3\times 10^{15}}$ | $\mathrm{1.0\times 10^{16}}$
Table 2:  Comparison of changes in deep acceptor (DA) trap density of of three
BCE a-IGZO TFTs with different etch process conditions (from Fig. 3b). TFT
$\mathrm{I_{D}-V_{G}}$ transfer curve turn-on voltage shifts ($\mathrm{\Delta
V_{ON}}$) shift right to predict the DA trap density ($\mathrm{N_{DA,ID-VG}}$)
that is also measured directly by the UBPC method ($\mathrm{N_{DA,UBPC}}$).

Table II compares values of $\mathrm{N_{DA,ID-VG}}$ and $\mathrm{N_{DA,UBPC}}$
for the three BCE a-IGZO TFTs plotted in Fig. 2(d). Note that the UBPC
estimate is invariably larger, as UBPC measures non-electrically active
valence band tail donor-like traps.[6, 14]

The UBPC spectra provided in Fig. 2(b-c) suggest that: (i). a standard BCE wet
etch process results in the formation of a Gaussian zinc vacancy state
centered at $\mathrm{\sim-2.2}$ eV (Zn-8), as well as a slight increase in the
Urbach energy; (ii). zinc vacancy state formation effect is particularly
severe for BCE TFTs with Mo source-drain electrodes; and (iii). the zinc
vacancy concentration can be reduced through the use of less-damaging etch
methods. The presence of this zinc vacancy peak and its particular
susceptibility to form in a-IGZO TFTs processed using a Mo S/D wet etch is
consistent with previously published work[25, 22, 23, 24] on BCE etch damage.
In Supplemental Materials Fig. S1, we provide the UBPC DoS for 6 BCE and 2 TG
a-IGZO TFTs subjected to various processing conditions, along with the
averaged BCE and TG DoS across all the devices studied. For all BCE devices,
the Zn-8 peak is larger than that observed for any of the TG TFTs.

A number of strategies have been explored to mitigate deleterious effects of
the etch process on BCE a-IGZO TFT electrical operation: careful selection of
source/drain metal;[23] plasma treatments intended to reduce metal
residues;[29] the use of etch stop layers (ESL);[22, 64, 23, 65] less damaging
wet etchants such as $\mathrm{{H_{2}}{O_{2}}}$;[66] or through adopting more
complex device architectures such as a dual-gate TFT.[67] A comparison of an
as-grown BCE TFT to a BCE TFT with an ESL is included in Supplemental
Materials Fig. S2. UBPC measurements on ESL device show a smaller zinc vacancy
DoS peak and improved electrical performance compared to an as-grown TFT.

###  III.3 Plasma treatments of a-IGZO TFTs

---
Figure 4:  Plasma processing of a-IGZO TFTs. (a) $\mathrm{n^{+}}$ doping of
the active channel of a top-gate a-IGZO TFT via plasma treatment or ion
implantation prior to deposition of the top-gate electrode. (b) The drain
current-gate voltage ($\mathrm{I_{D}-V_{G}}$) transfer curve of an as-grown
top-gate a-IGZO TFT compared to those subjected to an $\mathrm{Ar^{+}}$ ion
implantation or He plasma active channel pre-treatment. (c) The
$\mathrm{I_{D}-V_{G}}$ transfer curves of three $\mathrm{n^{+}}$ a-IGZO active
channel TFTs subjected to different plasma treatments. (d) Subgap density of
states ($\mathrm{cm^{-3}eV^{-1}}$) of as-grown and plasma-treated top-gate
TFTs whose $\mathrm{I_{D}-V_{G}}$ transfer curves) are plotted in (b-c).
(Inset) Linear ordinate scaling of DoS in the oxygen vacancy region of the
subgap.

Figure 4(a) depicts the fabrication of a $\mathrm{n^{+}}$ active channel TG
a-IGZO TFT via plasma or ion implantation treatment. In (Panel (1)), an a-IGZO
channel deposited onto a glass substrate with a $\mathrm{SiO_{2}}$ buffer
layer is subjected to a plasma or ion implantation treatment, resulting in a
$\mathrm{n^{+}}$ a-IGZO active channel. In (Panel (2)), source-drain
electrodes and the top-gate are patterned or deposited onto the
$\mathrm{n^{+}}$ a-IGZO active channel to form the $\mathrm{n^{+}}$ a-IGZO
active channel TFT. Note that in this process, the entire active channel is
subjected to the $\mathrm{n^{+}}$ doping treatment.

In Fig. 4(b), $\mathrm{log(I_{D})-V_{G}}$ transfer curves are compared for
three TG a-IGZO TFTs in which the channel is as-grown (black) or is subjected
to an $\mathrm{Ar^{+}}$ ion implantation (green) or a He plasma (blue)
treatment to obtain a $\mathrm{n^{+}}$ a-IGZO active channel. Arrows represent
the direction of hysteresis. Compared to the as-grown a-IGZO TFT, the plasma-
processed a-IGZO TFTs are strongly depletion-mode, with large negative turn-on
voltages of $\mathrm{<-20}$ V.

Figure 4(c) plots the $\mathrm{I_{D}-V_{G}}$ transfer curves of three TG
a-IGZO TFTs whose active channel is subjected to treatment by
$\mathrm{Ar^{+}}$ ion implantation (green), He plasma (blue), or Ar plasma
(red). The $\mathrm{Ar^{+}}$ ion-implanted and He plasma-treated TFTs exhibit
no measurable hysteresis when plotted on a linear ordinate scale (Fig. 4(c)),
and only a very small amount of clockwise hysteresis when plotted on a
logarithmic scale (Fig. 4(b)). In contrast, the Ar plasma-treated TFT
possesses a large amount of hysteresis and, notably, this hysteresis is
counterclockwise. Typically, clockwise hysteresis in an n-channel TFT is
ascribed to electron trapping, while counterclockwise hysteresis is attributed
to ion migration.[68] Thus, the counterclockwise hysteresis witnessed for the
Ar plasma-treated TFT is tentatively attributed to ion migration. However, we
note that this case is unusual since ion migration normally occurs within the
gate insulator rather than the semiconductor.

The deleterious effects of plasma damage to the channel layer of an a-IGZO TFT
are unambiguously revealed by UBPC, as shown in Fig. 4(d) and summarized in
Table III. Plasma processing dramatically increases the amplitude of all six
oxygen vacancy peaks. $\mathrm{Ar^{+}}$ ion implantation and He plasma
treatments increase the amplitudes modestly of the shallow O-1 and O-2 deep
traps by a factor of less than an order of magnitude. Ar plasma damage is the
most dramatic, increasing the amplitudes of all of the oxygen vacancy subgap
peaks by well over two orders of magnitude.

In addition to increasing the amplitude of the oxygen vacancy subgap peaks,
the valence band Urbach energy increases after plasma processing. This Urbach
energy increase is relatively modest for the $\mathrm{Ar^{+}}$ ion
implantation or He plasma treatment - from 117 $\pm$ 3 meV (as-grown) to 133
$\pm$ 8 meV ($\mathrm{Ar^{+}}$ ion implantation) - and is more significant for
the Ar plasma treatment at $\mathrm{E_{U}=}$ 180 $\pm$ 9 meV. The linear
ordinate density of states plot included as an inset to Fig. 4(d) shows how
much more damaging the Ar plasma treatment is than either the
$\mathrm{Ar^{+}}$ ion implantation or the He plasma treatment. After Ar plasma
treatment, the anion sublattice appears to be so heavily damaged that it is
not surprising that a TFT channel layer subjected to such abuse would exhibit
$\mathrm{I_{D}-V_{G}}$ transfer curve counterclockwise hysteresis due to ion
migration (perhaps by hydrogen) on its sub-lattice.

Table III reports the strongest oxygen deep acceptor DoS peak (
$\mathrm{\times 10^{16}}$ $\mathrm{cm^{-3}eV^{-1}}$) as obtained from the UBPC
measurement data plotted in Fig. 3(d). As the $\mathrm{I_{D}-V_{G}}$ curves
plotted in Fig. 3(c) indicate, all of the plasma-treated or ion-implanted TFTs
experienced large negative shifts in the $\mathrm{I_{D}-V_{G}}$ curve
$\mathrm{V_{ON}}$ curve ranging from $\mathrm{\sim-10-(-30)}$ V. This trend is
consistent with $\mathrm{n^{+}}$ doping of the active channel via the
$\mathrm{Ar^{+}}$ ion implantation or He/Ar plasma treatments.

A primary application of ion implantation or plasma treatment is the formation
of $\mathrm{n^{+}}$ source/drain regions at the active channel edge; after
plasma treatment (or ion implantation), these $\mathrm{n^{+}}$ doped regions
function as low contact resistance source-drain regions. The UBPC DoS spectra
and $\mathrm{I_{D}-V_{G}}$ transfer curves in Fig. 4 confirm that treatments
that induce a moderate amount of disorder in the active channel material
(forming shallow donor oxygen vacancy states) such as $\mathrm{Ar^{+}}$ ion
implantation and He plasma are useful for achieving the desired
$\mathrm{n^{+}}$ doping effect.

|  $\mathrm{E-E_{C}}$(eV) | As-grown |  $\mathrm{Ar^{+}}$ ion | He plasma | Ar plasma
---|---|---|---|---|---
$\mathrm{O-1}$ | $-0.30$ | $1.1$ | $3.2$ | $3.2$ | $500$
$\mathrm{O-2}$ | $-0.70$ | $1.7$ | $3.7$ | $7.0$ | $210$
$\mathrm{O-3}$ | $-1.30$ | $3.2$ | $27$ | $23$ | $590$
$\mathrm{O-6}$ | $-1.95$ | $0.8$ | $190$ | $140$ | $520$
$\mathrm{O-7}$ | $-2.45$ | $5.9$ | $420$ | $500$ | $1000$
$\mathrm{E_{U}}$(meV) |  | $117\pm 3$ | $133\pm 8$ | $135\pm 6$ | $180\pm 9$
Table 3:  Comparison of density of states peak maximum amplitudes ($\times
10^{16}\mathrm{cm^{-3}eV^{-1}}$) and Urbach energies (meV) from Fig. 4(d) for
four top-gate a-IGZO TFTs with the full-channel subjected to different plasma
and ion-etch processes.
---
Figure 5:  UBPC DoS spectra comparing different a-IGZO growth conditions
plotted for a BCE 111 (black) and a BCE Zn-rich (blue) a-IGZO growths. Fitted
DoS peak are plotted as shaded regions. (Inset) Drain current-gate voltage
($\mathrm{I_{D}-V_{G}}$) transfer curves for a BCE 111 a-IGZO and a BCE Zn-
rich a-IGZO TFT.

###  III.4 a-IGZO channel stoichiometry variations

|  $\mathrm{E-E_{C}}$(eV) | 111 | Zn-rich
---|---|---|---
$\mathrm{O-2}$ | $-1.05$ | $0.25$ | $1.45$
$\mathrm{O-3}$ | $-1.25$ | $4.6$ | $32$
$\mathrm{O-4}$ | $-1.40$ | $2.2$ | $32$
$\mathrm{O-5}$ | $-1.75$ | $2.45$ | $22$
$\mathrm{O-6}$ | $-1.90$ | $11.9$ | $117$
$\mathrm{O-7}$ | $-2.50$ | $59$ | $5.2$
$\mathrm{E_{U}}$(meV) |  | $105$ | $78$
Table 4:  Comparison of the DoS peak maximum amplitudes ($\times
10^{16}\mathrm{cm^{-3}eV^{-1}}$) from Fig. 5(d) for [111] and Zn-rich growth
recipes for BCE a-IGZO TFTs.

Figure 5 plots the density of states (dots: UBPC experimental data, shaded
regions: simulation) of a BCE a-IGZO TFT with a stoichiometric (1:1:1 molar %
$\mathrm{In_{2}O_{3}/Ga_{2}O_{3}/ZnO}$ ratio) active channel composition (111
a-IGZO, black) against a BCE a-IGZO TFT with a higher molar proportion of Zn
constituent atoms than In and Ga (Zn-rich, blue). The (Inset) of Fig. 5 plots
the drain current-gate voltage ($\mathrm{I_{D}-V_{G}}$) transfer curve of the
111 as-grown and Zn-rich TFTs. Table IV reports the DoS peak characteristics
for 111 and Zn-rich a-IGZO TFTs.

The DoS peak amplitudes of the O-3, O-4, O-5, and O-6 peaks are significantly
larger for the Zn-rich a-IGZO TFT compared to the 111 a-IGZO TFT, but the O-7
peak is smaller. The $\mathrm{I_{D}-V_{G}}$ transfer curve of the Zn-rich
a-IGZO TFT also exhibits a lower drain current, a positive shift in the turn-
on voltage, and a poorer sub-threshold slope than the 111 a-IGZO TFT. These
trends are consistent with an electron trapping-induced reduction of TFT
electrical performance due to the enhanced density of subgap oxygen vacancy
states. The appreciable increase in shallow oxygen vacancy states is
particularly concerning with respect to TFT operation. Surprisingly, the
valence band Urbach energy of the Zn-rich TFT is $\mathrm{\sim 30\%}$ smaller
than that of the 111 a-IGZO TFT.

###  III.5 Hydrogen incorporation into a-ITGZO TFTs

---
Figure 6:  (a) Drain current-gate voltage ($\mathrm{I_{D}-V_{G}}$) transfer
curves, (b) near conduction band total integrated trap density,
$\mathrm{N_{TOT}}$, and (c) near valence band DoS for two a-ITGZO BCE TFTs
possessing different concentrations of hydrogen. (c, Inset) UBPC DoS plotted
across full bandgap (logarithmic scale).

Figure 6 displays a comparison between two a-ITGZO TFTs possessing different
concentrations of hydrogen. $\mathrm{I_{D}-V_{G}}$ transfer curves (Fig. 6(a))
shows that the larger incorporated hydrogen concentration (orange curve)
shifts the turn-on voltage $\mathrm{-3.6}$ V, corresponding to an estimated
increase in the hydrogen concentration of $\mathrm{\Delta[H]_{ID-VG}=2.8\times
10^{17}}$ $\mathrm{cm^{-3}}$, where $\mathrm{\Delta[H]_{ID-VG}=(C_{I}\Delta
V_{ON}/q)^{1.5}}$ and $\mathrm{C_{I}=19.2}$ $\mathrm{nFcm^{-2}}$ is the
insulator capacitance density. (Since hysteresis is present in the
$\mathrm{I_{D}-V_{G}}$ transfer curves shown in Fig. 6(a), $\mathrm{V_{ON}}$
is estimated as the midpoint between the right-going and left-going turn-on
voltages.) The negative turn-on voltage shift indicates that the incorporated
specie (hydrogen) behaves as a donor.

Since donor ionization energies are normally found nearest to the conduction
band, Fig. 6(b) is included in order to demonstrate that the UBPC-estimated
trap density is extremely small (less than $\mathrm{10^{14}}$
$\mathrm{cm^{-3}}$), and that the near conduction band trap distribution is
quite similar for the two a-ITGZO BCE TFTs under consideration. These
observations support our contention[14] that hydrogen does not behave as a
normal shallow donor when incorporated into an amorphous oxide semiconductor,
such as a-ITGZO.

Figure 6(c) reveals an enhanced density of states at
$\mathrm{E-E_{C}\approx-2.7}$ eV for the high hydrogen (orange) curve,
compared to the low H (black) curve. This corresponds to an increase in the
concentration of $\mathrm{{[{O_{O}^{2-}}{H^{+}}]}^{1-}}$ defect complexes (or,
equivalently, $\mathrm{OH^{-}}$) with increasing hydrogen incorporation. This
increase in hydrogen between the black and orange curve can be quantified via
UBPC, i.e.,
$\mathrm{\Delta[H]_{UBPC}=N_{TOT}(E_{g})|_{\begin{subarray}{c}\text{orange}\\\
\text{curve}\end{subarray}}-N_{TOT}(E_{g})|_{\begin{subarray}{c}\text{black}\\\
\text{curve}\end{subarray}}}$ $\mathrm{=9.2\times 10^{17}cm^{-3}}$
$\mathrm{-6.0\times 10^{17}cm^{-3}}$ $\mathrm{=3.2\times 10^{17}cm^{-3}}$.
This value of $\mathrm{\Delta[H]_{UBPC}}$ ($\mathrm{3.2\times
10^{17}cm^{-3}}$) is quite similar to that estimated previously for
$\mathrm{\Delta[H]_{ID-VG}}$ ($\mathrm{2.8\times 10^{17}cm^{-3}}$), indicating
that a correlation exists between the enhancement in density of states and the
enhancement in electrically-active donor activity with increasing hydrogen
concentration. However, note that $\mathrm{\Delta[H]_{UBPC}>\Delta[H]_{ID-
VG}}$ since the UBPC-estimated density of states enhancement includes a
contribution associated with valence band tail state density arising from
enhanced anion sublattice disorder, as well as from a simple increase in the
$\mathrm{{[{O_{O}^{2-}}{H^{+}}]}^{1-}}$ defect complex density centered at
$\mathrm{E-E_{C}=-2.7}$ eV, due to direct incorporation of hydrogen into the
amorphous network.[14]

Simple charge balance considerations are useful for rationalizing turn-on
voltage trends similar to those presented in Fig. 6(a).[6] Enhancement-mode
operation, in which $\mathrm{V_{ON}}$ is positive, is witnessed for the two
curves included in Fig. 6(a). Charge balance describing this type of behavior
likely arises from $\mathrm{N_{DD}^{+}(E_{F})=N_{DA}-N_{SD}}$ where
$\mathrm{N_{DD}^{+}}$ refers to positively ionized deep donors, the
concentration of which is controlled by the position of the Fermi level,
$\mathrm{N_{DA}}$ is the density of deep acceptors, and $\mathrm{N_{SD}}$ is
the density of shallow donors. Identifying deep donors as oxygen vacancies
that are distributed across the upper portion of the a-ITGZO bandgap (see
Inset to Fig. 6(c)), deep acceptors as zinc vacancies located at
$\mathrm{E-E_{C}\approx-2.3-(-2.4)}$ eV, and shallow donors as
$\mathrm{{[{O_{O}^{2-}}{H^{+}}]}^{1-}}$ defect complexes centered at
$\mathrm{E-E_{C}\approx-2.8}$ eV, enhancement-mode behavior is a balancing act
between zinc vacancies and hydrogen. Although
$\mathrm{{[{O_{O}^{2-}}{H^{+}}]}^{1-}}$ defect complexes are certainly not
energetically ‘shallow’ since they are centered at
$\mathrm{E-E_{C}\approx-2.8}$ eV, they can be considered to be ‘shallow’ from
the perspective of charge balance assessment since they remain ionized,
independent of the position of the Fermi level. This odd behavior is due to
the non-equilibrium nature of the hydrogen donor, in which hydrogen ionization
occurs prior to its incorporation into the amorphous network.[14]

When the zinc vacancy density is much larger than that of incorporated
hydrogen, $\mathrm{N_{DD}^{+}(E_{F})\approx N_{DA}}$, so that strongly
enhancement-mode behavior obtains (black curve of Fig. 6(a)),
$\mathrm{V_{ON}}$ is positive and large, and $\mathrm{E_{F}}$ is positioned
deep in the gap (perhaps $\mathrm{-1.5}$ to $\mathrm{-2}$ eV), leaving many
oxygen vacancy traps empty (empty traps ($\mathrm{N_{DD}^{+}}$) are
responsible for enhancement-mode behavior). In contrast, when the zinc vacancy
and incorporated hydrogen concentrations are similar,
$\mathrm{N_{D}^{+}(E_{F})\approx 0}$, such that $\mathrm{V_{ON}\approx 0}$ V,
and $\mathrm{E_{F}}$ is positioned about $\mathrm{0.15-0.3}$ eV below the
conduction band mobility edge, and most oxygen vacancy traps are filled.
Finally, if the incorporated hydrogen concentration exceeds that of the zinc
vacancy concentration, then depletion-mode behavior occurs, as described by a
different charge balance relationship, $\mathrm{n(E_{F})\approx
N_{SD}-N_{DA}}$, where n is the free electron density, as controlled by the
position of the Fermi level.

##  IV CONCLUSIONS

The subgap DoS measured by ultrabroadband photoconduction (UBPC) method
reveals systematic trends in AOS subgap trap density that correlate with
different TFT architectures, doping treatments, and AOS growth compositions.
Notably, we observed the following: 1, a Zn vacancy peak centered at
$\mathrm{-2.2}$ eV is introduced to back channel etch a-IGZO TFTs as a result
of the source/drain metal etch process, and its peak density can be modulated
via different etch conditions; 2, Ar and He plasma and $\mathrm{Ar^{+}}$ ion
implantation-treatment of the a-IGZO active channel enhance the peak densities
of deep-donor oxygen vacancy states up to $\mathrm{>100}$X; 3, Zn-rich a-IGZO
TFTs exhibit larger deep oxygen vacancy peaks and significantly worse
electrical performance compared to a-IGZO with a 1:1:1 stoichiometric molar
proportion of constituent cations; and 4, as previously observed for a-IGZO,
[14] hydrogen incorporation in a-ITGZO rigidly shifts the
$\mathrm{I_{D}-V_{G}}$ transfer curve negative, and is accompanied by a
corresponding increase in the $\mathrm{{[{O_{O}^{2-}}{H^{+}}]}^{1-}}$ complex
peak density centered at $\mathrm{-2.8}$ eV.

The defect DoS obtained with the UBPC method has both high 106 signal to noise
sensitivity and a broad energy range not yet obtainable by established purely
optical and XPS/UPS methods. This newly demonstrated capacity of UBPC to
predict defects arising from on-chip TFT processing and growth recipes further
suggests its use as an analytical tool in AOS TFT development. The different
categories of defects (acceptor, donor, and interstitial hydrogen) that
compose the subgap of AOS impact both the TFT $\mathrm{I_{D}-V_{G}}$ transfer
curve turn-on voltage and hysteresis. The next challenge is to extend the UBPC
method over the conduction band Urbach tails to complete the connection
between subgap trap density and performance TFT metrics. The clear DoS trends
with TFT production processing demonstrate that UBPC is an emerging post-
assembly defect characterization method that can work down to the single-pixel
limit.

###### Acknowledgements.

Acknowledgments: This work was in part supported by the NSF Grants
DMR-1920368. We would like to thank Jessica Waymire for her contribution to
the optical photoluminescence of a-IGZO measurements.

Supporting Information Available: Supplemental experiment DoS curves showing a
statistical comparison between many TFT amorphous oxide devices.

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