Materials Science in Semiconductor Processing 158 (2023) 107344
Contents lists available at ScienceDirect
Materials Science in Semiconductor Processing
journal homepage: www.elsevier.com/locate/mssp
Metal oxide semiconductor nanowires enabled air-stable ultraviolet-driven
synaptic transistors for artificial vision
Ruifu Zhou a, Wenxin Zhang b, Haofei Cong b, Yu Chang b, Fengyun Wang b, **, Xuhai Liu a, *
a
b
College of Microtechnology & Nanotechnology, Qingdao University, Qingdao, 266071, China
College of Physics, Qingdao University, Qingdao, 266071, China
A R T I C L E I N F O
A B S T R A C T
Keywords:
ZnSnO
Nanowire
Device stability
In memory computing
Bionic vision
The multi-terminal transistor can provide reliable platform to construct artificial synapses with various benefits.
In particular, synaptic transistors based on wide-bandgap metal oxide semiconductors (MOSs) offer opportunities
to realize ultraviolet (UV) artificial vision. In this work, via a low-cost electrospinning technique, we have
fabricated bio-inspired synaptic transistors based on zinc tin oxide (ZnSnO) nanowires, which can be effectively
tuned by UV laser to achieve air-stable synaptic characteristics, such as excitatory post-synaptic current, pairedpulse facilitation, and learning-forgetting process based on long-term potentiation. The adsorption/desorption of
oxygen molecules governs the generation of free charge carriers in the ZnSnO nanowires. Via the combination of
transmission electron microscopy and X-ray diffraction measurements, we found that the revealed SnO2/ZnO
heterostructures can effectively mitigate the recombination of UV-induced free carriers in the ZnSnO nanowires.
Furthermore, based on the synaptic characteristics obtained from ZnSnO nanowires, we present several appli­
cations relevant to the UV artificial vision. We believe this study can provide insights into importance of het­
erostructures in synaptic transistors based on low-dimension MOSs with superior environmental stability and
various applications.
1. Introduction
The past decade has witnessed a remarkable research growth in bioinspired in-memory computing neuromorphics, which opens an entirely
new field of cutting-edge hardware for distributed and bio-resembled
computing systems [1,2]. To realize robust neuromorphic circuit-level
and even system-level design, it is essential to construct a reliable
building block of artificial synapse with combined benefits, such as su­
perior synaptic weight controllability and nonvolatile synaptic function,
as well as versatile controllable manners. In this regard, multi-terminal
synaptic transistors (MTSTs) with these plausible features can exert
positive impacts [3–5].
External signals in various energy forms are the first necessities to
realize in-memory computations based on MTSTs. Ongoing in­
vestigations aim to emulate the five senses through realizing combined
capabilities of signal sensing and computing as well as storing with
MTSTs, including tactility [6], taste [7], olfactory [8], auditory [9], and
vision [10]. These sensing-computing-storing determined five senses are
not only paramount for biological species, but also can issue
transformative impacts on embodied intelligence of next-generation
artificial systems [11,12]. Among them, most of the brain-processed
information is acquired by the visual sense [13], which indicates the
broad importance of bionic vision in developing artificial systems. In
specific, the ultraviolet (UV) vision not only holds the key for many
biological species to navigate location and avoid predator [14–16], but
also can be vital for artificial systems applied in astronomical observa­
tions and terrestrial planet explorations [17,18]. However, along with
the rapid development of bionic vision covering infrared and human
visible spectra [19–22], slower albeit parallel advances have been ach­
ieved in the UV regime using MTSTs.
This relatively slow stride of the synaptic transistors driven by UV
laser can be accelerated by applying suitable wide-bandgap metal oxide
semiconductors (MOSs) with multiple advantages, such as air stability,
cost effectiveness, process compatibility and superior optoelectronic
characteristics
[23–25],
especially
considering
the
large
surface-to-volume ratio of one-dimensional nanowire configuration
[26–28]. Specifically, we have recently applied a manufacturing process
that combines low-temperature combustion and electrospinning
* Corresponding author.
** Corresponding author.
E-mail addresses: fywang@qdu.edu.cn (F. Wang), xuhai@qdu.edu.cn (X. Liu).
https://doi.org/10.1016/j.mssp.2023.107344
Received 8 December 2022; Received in revised form 11 January 2023; Accepted 16 January 2023
Available online 24 January 2023
1369-8001/© 2023 Elsevier Ltd. All rights reserved.
R. Zhou et al.
Materials Science in Semiconductor Processing 158 (2023) 107344
technique to prepare UV photodetectors based on indium zinc oxide
(InZnO) nanowires, and the corresponding electrical response can be
adjusted by configuring multi-cation MOSs [29,30]. Moreover, we have
also fabricated electrical-driven synaptic transistors based on InZnO
nanowires, in which however, the environmental stability of the device
has been limited by the application of ion-rich electrolyte during the
electrical biasing [31]. Also importantly, due to the urgent need to
replace the low-content indium in the earth (0.25 ppm), zinc tin oxide
(ZnSnO) without toxic element has been treated as an ideal semi­
conductor to substitute for indium-based MOSs [32].
With the assistance of low-cost electrospinning method, we have
fabricated bio-inspired synaptic transistors based on ZnSnO nanowires,
which can be effectively adjusted by 375 nm UV laser to obtain air-stable
synaptic properties. In order to obtain the appropriate electrical signals,
such as excitatory post-synaptic current (EPSC), paired-pulse facilitation
(PPF), and learning-forgetting process based on relative long-term
potentiation (LTP), UV light with various pulse number and light in­
tensity was applied. The adsorption/desorption of oxygen molecules
governs the generation of free charge carriers in the ZnSnO nanowires.
Via the combination of transmission electron microscopy (TEM) and Xray diffraction (XRD) measurements, the revealed SnO2/ZnO hetero­
structures can effectively mitigate the recombination of UV-induced free
carriers in the ZnSnO nanowires. Furthermore, the high binding energy
of Sn–O and Zn–O bonds can be an important factor to positively impact
the device stability. Moreover, based on the synaptic characteristics
obtained from ZnSnO nanowires, we present several applications rele­
vant to the UV artificial vision. This study provides insights into
importance of heterostructures in synaptic transistors based on lowdimension MOSs with superior environmental stability and various
applications.
Fig. 1. Material characterization of ZnSnO nanowires. (a) SEM image of ZnSnO nanowires, with the inset showing a typical HRTEM image of a single ZnSnO
nanowire. (b)–(d) TEM image, HRTEM image with detailed microstructure, and SAED pattern of a ZnSnO nanowire, respectively. (e) Corresponding XRD pattern of
the ZnSnO nanowire. (f) XPS spectra of the ZnSnO nanowire. From left to right: Zn 2p, Sn 3d, and O 1s. (g) EDS elemental mapping of a ZnSnO nanowire.
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Materials Science in Semiconductor Processing 158 (2023) 107344
2. Results and discussion
the interplanar spacing measured to be 0.28 nm, it is close to the ideal
spacing of the hexagonal wurtzite ZnO (100) plane. The HRTEM image
can well indicate the phase separation between SnO2 and ZnO in ZnSnO
nanowires. Accordingly, the uniform crystalline grains in the ZnSnO
nanowire can be confirmed by the selected area electron diffraction
(SAED pattern demonstrated in Fig. 1(d). We have carried out XRD
measurement to further confirm the crystalline characterization of
ZnSnO nanowires, as demonstrated in Fig. 1(e). The diffraction peaks at
(110) and (211) in Fig. 1(e) correspond to the rutile SnO2
(PDF#41–1445), whereas the diffraction peak at (100) can be referred
to the wurtzite ZnO (PDF#36–1451). To complement the stoichiometry
of ZnSnO nanowires, X-ray photoelectron spectroscopy (XPS) analyses
of the ZnSnO nanowires were performed. As shown in Fig. 1(f), the
figures from left to right-hand side refer to the XPS spectra of Zn 2p, Sn
3d, and O 1s, respectively. Regarding Zn 2p, it can be observed that Zn
2p3/2 and Zn 2p1/2 locate at 1022.1 and 1045.1 eV, indicating the +2
valence state of Zn [34]. As for the Sn 3d, we can locate the 3d5/2 and Sn
3d3/2 at 486.2 and 494.7 eV, respectively, which suggests the oxidation
state of +4 Sn atoms [35]. When it comes to O 1s, the peak located at
530.1 eV and 531.6 eV can be denoted as oxygen atoms restricted within
the M − O lattices, and adjacent to the oxygen vacancies, respectively.
The peak intensity at 530.1 eV is higher than other peaks, which in­
dicates the high crystallinity of the samples. The peak located at 533.6
2.1. Material characterization
Fig. 1(a) presents the scanning electron microscopy (SEM) image of a
typical device area occupied by ZnSnO nanowire network, in which the
straight nanowires have been fabricated via rigorously tuned electro­
spinning parameters and annealing conditions. It can be referred to
Experimental section for details and our previously published work for
correlated fabrication process [29,33]. The inset of Fig. 1(a) illustrates a
zoomed-in SEM image of an individual ZnSnO nanowire with a diameter
measured to be 63.2 nm. The diameter of the ZnSnO nanowires mostly
fall into the range of 50–90 nm, as shown in Fig. S1. It can be observed
that the nanowire in Fig. 1(a) exhibits smooth surface without any
fracture, which indicates the reliability of our optimal electrospinning
technique for obtaining metal oxide nanowires with ideal morphology.
To investigate the microstructure of the ZnSnO nanowires, we have
also conducted TEM measurements. Fig. 1(b) demonstrates a TEM image
of an as-prepared ZnSnO nanowire, in which the electrospun nanowire is
composed of stacked crystalline grains. Furthermore, the highresolution TEM (HRTEM) image in Fig. 1(c) presents the interplanar
spacings of 0.33 nm and 0.17 nm, which can be indexed to the (110) and
(211) planes of tetragonal rutile SnO2 lattice, respectively. Regarding
Fig. 2. Device configuration and synaptic characteristics emulated by optically driven ZnSnO nanowire transistors. (a) Schematic diagram of the human visual
system, with highlighted synapse between pre-synaptic neuron and post-synaptic neuron. (b) Configuration of an UV-light driven multi-terminal synaptic transistor
based on ZnSnO nanowires. (c) EPSC at Vds = 1 V and Vgs = 0 V, excited by 375 nm UV light. The UV light intensity and pulse width are 1.29 mW cm− 2 and 500 ms,
respectively. Inset: schematic of a typical action potential in a bio-neural system, which is here used to compared with the optically driven EPSC. (d)–(e) EPSC
variations triggered by different UV pulse width and intensity, respectively. (f) PPF driven by a pair of UV light pulses (6.58 mW cm− 2) with pulse width of 200 ms
and pulse interval of 100 ms. Inset: PPF dependence on the UV pulse interval between the two consecutive pulses, with peaks denoted by A1 and A2, respectively. (g)
Gradual transition from short-term potentiation to relative long-term potentiation in response to various UV pulse number (pulse width = 500 ms, Δt = 500 ms, light
intensity = 3.43 mW cm− 2). (h) Small decay of EPSCs measured in a period of two months (pulse width = 500 ms, light intensity = 1.29 mW cm− 2).
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Materials Science in Semiconductor Processing 158 (2023) 107344
eV can be attributed to chemisorbed OH− molecules on the surface of the
ZnSnO nanowires [36]. Fig. S2 demonstrates the UV–Vis absorption
spectrum of the ZnSnO nanowires with the absorption wavelength
ranging from 200 to 390 nm, which indicates the capability of the
ZnSnO nanowires to detect wide-spectrum high energy light sources.
Moreover, all the zinc, tin and oxygen atoms are homogeneously
distributed within the ZnSnO nanowire, as demonstrated in the energy
dispersive X-ray spectroscopy (EDS) mapping of Fig. 1(g).
)
)
(
(
Δt
Δt
+ C2 exp −
PPF = C0 + C1 exp −
τ1
τ2
(1)
where C1 and C2 stand for initial facilitation magnitudes, and τ1 and τ2
are relaxation time constants, respectively. As illustrated in Fig. 2(f), the
fitting results demonstrate that τ1 and τ2 are here 0.12 s and 1.35 s,
respectively, which are in accordance with values derived from the
biological system.
Fig. 2(g) describes the pulse-number dependent EPSCs of the ZnSnO
synaptic transistors. When the UV pulse number is twenty or less than
twenty, the measured current can be enhanced with increasing the pulse
number and gradually decays to the initial current value. In comparison,
when the UV pulse number exceeds twenty, the measured current
cannot return to the initial value within the same period. This can echo a
bio-resembled point that the short-term potentiation (STP) can be
transformed to LTP through repetitive stimuli [40,41]. However, it
should be noted that the time frame required for LTP is generally gauged
by hours in a biological neural system, instead of minutes or seconds.
Therefore, in this work, the measured transition between the two states
should be denoted as from STP to relative LTP [40,41]. Nevertheless,
according to this transition from STP to relative LTP, we have emulated
the bio-resembled learning-forgetting-relearning process based on the
UV-driven ZnSnO nanowire synaptic transistors, as shown in Fig. S3. To
be specific, the three learning processes in Fig. S3 are highlighted by the
purple color, and from left to right-hand side the learning processes
require 40, 18, and 14 pulses to reach the maximum current value,
respectively. This is because that the two 50-seccond forgetting pro­
cesses can be regarded as the buffer medium to decelerate the current
decay from the peak current value. Moreover, the environmental sta­
bility is critical to the synaptic transistors to be integrated into circuits
for practical applications. Fig. 2(h) demonstrates the EPSCs of an ZnSnO
nanowire transistor measured at different time within two months. It
can be observed that the EPSC has been decayed to approximately 93%
of its initial value after eight weeks, indicating the good environmental
stability of the synaptic transistors based on ZnSnO nanowires. The high
binding energy of 528 kJ mol− 1 Sn–O and 159 kJ mol− 1 Zn–O can be
speculated to be an important factor to positively impact the device
stability [42].
As previously discussed regarding TEM and XRD measurements in
Fig. 1, it is revealed that SnO2 and ZnO co-exist in the ZnSnO nanowires.
Due to the different energy band of SnO2 and ZnO, type-II hetero­
structure can be formed in the ZnSnO nanowires with SnO2/ZnO het­
erostructures. The adsorption/desorption of oxygen molecules governs
the generation of free carriers in the ZnSnO nanowires [43]. Fig. 3(a)
demonstrates the schematic illustration of oxygen adsorption–induced
charge trapping. Oxygen molecules (O2) adsorbed onto the surfaces of
nanowires convert into negatively charged ions (O-2) by capturing free
electrons from the ZnSnO nanowires in ambient condition with the
scheme of O2 + e− → O-2. Therefore, the depletion layer can be formed
on the nanowire surface, leading to a low conductivity. Upon exposure
to UV light, the electron-hole pairs can be generated, and the photo­
generated holes migrate to the surface of the nanowires to discharge the
adsorbed oxygen ions via a reaction of O-2+ h+ → O2, reducing the
depletion barrier thickness and increasing the concentration of free
carriers. Hence, the photocurrent in the channel can be dramatically
increased. Meanwhile, a type-II heterostructure with a staggered align­
ment at the heterojunction is formed in the ZnSnO nanowires, because
the valence-band energy level of ZnO is higher than that of SnO2, as
shown in Fig. 3(b) [44]. The photogenerated electrons move towards
SnO2, whereas the photogenerated holes move towards ZnO, thus
effectively promoting the separation of the electrons and holes, as
illustrated in Fig. 3(c). Due to the presence of potential barriers between
SnO2 and ZnO, the photoexcited states are spatially separated when the
UV light is removed. These heterostructure barriers can then prevent the
recombination of electron-hole in the nanowires, which results in the
2.2. Device configuration and synaptic characterization
The biological synapse has been widely regarded as the inspiration
for multi-terminal synaptic transistors [2]. Fig. 2(a) illustrates a
simplified human visual system, in which the external light is firstly
received via human retina and then transmitted to the visual cortex. In
this process, the presynaptic neurons respond to stimulus and corre­
spondingly release neurotransmitters to couple with receptors in post­
synaptic neurons. This signal transmission process can be emulated by
three-terminal optoelectronic transistors, as shown in Fig. 2(b). To be
specific, the UV pulse is considered as the presynaptic signal, whereas
the ZnSnO nanowires act as the postsynaptic membrane, through which
the output current signals can be transmitted between the metal
electrodes.
In a bio-neural system, the signal transmission among neurons is
largely supported by long-range action potential, which is defined using
the resting membrane potential as the baseline [2], as shown in the inset
of Fig. 2(c). Therefore, to mimic the biological neural functions via
optically driven artificial components, it is paramount to emulate the
action potentials with the multi-terminal synaptic transistors. In this
regard, the electrical response registered upon an external UV stimula­
tion is treated as the EPSC, which can be regarded as the equivalence of a
biological action potential, as presented in Fig. 2(c). The EPSC can
rapidly reach the peak current of 89 pA under the illumination of 375
nm UV light at a small light intensity of 1.29 mW cm− 2. This could be
facilitated by the large surface-to-volume ratio of ZnSnO nanowires for
excessive photogenerated charge carriers. The amplitude and shape of
the EPSC are primarily determined by two factors. The first one is the
pulse width of the external UV light, and it can be observed that the
corresponding peak value and width of the EPSC increase accordingly
with increasing UV pulse width from 100 to 2000 ms, as demonstrated in
Fig. 2(d). The second factor is the light intensity of the UV stimulation.
As shown in Fig. 2(e), the EPSC peak is significantly enhanced with
increased UV pulse intensity that falls in the range of 0.73–6.58 mW
cm− 2. These variations of EPSC under different UV pulse width and in­
tensity can be attributed to the gradually enrichment of channel current
due to more photogenerated charge carriers.
After elaborating a single EPSC, we continue with discussing the
signal that is composed of two consecutive EPSCs, known as PPF [37],
which can be used for synaptic enhancement in information transfer and
bio-neural transmission. For instance, in hippocampal cultured neurons,
the PPF can be mediated by specific proteins, which suggests the op­
portunity to characterize the PPF effect on bio-circuit activities via
manipulating corresponding proteins [38]. As presented in Fig. 2(f), to
emulate the PPF behavior, the ZnSnO nanowire synaptic transistors have
been illuminated by two consecutive 375 nm UV light with pulse width
of 200 ms as well as intensity of 6.58 mW cm− 2. The peaks of EPSC
triggered by the first and the second UV pulses are denoted as A1 and A2,
respectively. In this case, the values of A2 and A1 are respectively 0.29
and 0.12 nA, with A2/A1 ratio (i.e., PPF index) calculated to be 242%.
This is in accordance with the biological scenario, in which the peak of
the second EPSC can be up to five times the value of the first [37]. The
inset of Fig. 2(f) shows that the PPF index decays exponentially with
increased interval (Δt) between the two consecutive UV pulses. The
double-exponential decay relation can suit this dependency of PPF value
on the time interval Δt as follows [39]:
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Materials Science in Semiconductor Processing 158 (2023) 107344
Fig. 3. (a) Schematic diagram of the oxygen adsorption induced charge trapping. (b)–(d) Schematic illustration of the energy band diagram for ZnSnO nanowires
under equilibrium, with illumination, and after UV illumination, respectively.
slow decay of the conductance, as demonstrated in Fig. 3(d) [45].
2.3. Applications
Various aspects can be heavily involved in the formation of UV
artificial vision, such as reliable logic gates for digital computation,
Fig. 4. Logic gates achieved by 375 nm UV-driven ZnSnO nanowire synaptic transistors. (a) Schematic diagram of programmable “AND” and “OR” logic gates. Input
UV: light intensity of 0.52 mW cm− 2 and optical pulse width of 3 s. Tuning UV: light intensity of 20 mW cm− 2 and optical pulse width of 5 s. Input Vg: amplitude of
0.5 V and electrical pulse width of 3 s. Tuning Vg: amplitude of 10 V and electrical pulse width of 5 s. Tuning UV switch the logic gate to “OR”, tuning Vg switch the
logic gate to “AND”. (b) Corresponding “AND” and “OR” logic gates achieved by the combination of optical and electrical stimulation. (c) “AND-OR” cycle for
five times.
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Materials Science in Semiconductor Processing 158 (2023) 107344
vision recognition accuracy, and sensing-memory-process system. In this
sub-section, based on the previously discussed synaptic characteristics
obtained from ZnSnO nanowires, we present several applications rele­
vant to the UV artificial vision.
amplitude positive gate voltage, the free electrons in the nanowires are
trapped by the positive defects on the ZnSnO/SiO2 surface, leading to
the decrease of carrier concentration and a switch from high to low
conductance state. In comparison, when the synaptic transistor is
exposed to high-power UV light, the trapped carriers can absorb enough
energy and released into the channel, leading to a high conductance
state. To verify the stability of our programmable “AND” and “OR” logic
gates, we have repeated the “AND-OR” cycle for five times and obtained
stable transition between these two logic gates, as demonstrated in Fig. 4
(c).
2.3.1. Programmable optoelectronic logic gates
Fig. 4(a) demonstrates a schematic illustration of programmable
“AND” and “OR” logic gates as well as the transition process between
them. Both the “AND” and “OR” gates can be obtained via combined
adjustment through an optical input of UV light and an electrical input
of gate voltage. To be specific, as shown in Fig. 4(a), the optical input of
UV light is denoted as Input UV with the light intensity of 0.52 mW cm− 2
and pulse width of 3 s, whereas the electrical input of gate voltage is
labeled as Input Vg with the amplitude of 0.5 V and pulse width of 3 s.
Both of Input UV and Input Vg are defined as “1” if it is turned on, or “0”
if it is turned off, as illustrated in Fig. 4(b). To obtain the “AND” logic
gate, the EPSC triggered by Input UV or Input Vg refers to “True” if the
triggered current is larger than a threshold of 0.02 nA (marked by red
dashed line), otherwise “False” if the current is less than 0.02 nA.
Therefore, regarding the “AND” gate highlighted by the blue color in
Fig. 4(b), the eventual EPSC triggered by a combined programmed
stimulation of Input UV and Input Vg is sequentially “00” (False), “10”
(False), “01” (False), and “11” (True). In contrast, regarding the “OR”
gate highlighted by purple in Fig. 4(b), the final EPSC triggered by the
same combined stimulation of Input UV and Input Vg is sequentially
“00” (False), “10” (True), “01” (True), and “11” (True).
This forward-transition from “AND” to “OR” can be realized by
illuminating the ZnSnO nanowires by UV light with higher intensity and
larger pulse width, which is in this scenario denoted as Tuning UV with
the intensity of 20 mW cm− 2 and pulse width of 5 s, as shown in Fig. 4
(a). In contrast, the backward-transition from “OR” to “AND” can be
enabled by applying an electrical pulse with larger voltage amplitude
and pulse width, which is accordingly denoted as Tuning Vg with the
amplitude of 10 V and pulse width of 5 s. The underlying mechanism of
the “AND-OR” transition can be speculated in the following. A large
number of positively charged traps are formed on the SiO2 surface due to
the Si–O dangling bonds and movable positive charges. With high
2.3.2. Hand-Written recognition
The linearity and symmetricity of the weight update determine the
accuracy of pattern recognition. Fig. 5(a) illustrates the LTP/LTD char­
acteristics of ZnSnO nanowire synaptic transistors. The channel
conductance gradually enhances with 100 consecutive light pulses with
pulse widths of 500 ms, Δt of 500 ms at a fixed power density of 1.29
mW cm− 2. Afterward, 100 consecutive electricity pulses (Vg = − 1 V,
width = 500 ms, Δt = 500 ms) are applied to the bottom gate to reduce
the channel conductance. The nonlinearity of the weight update in
Artificial Neural Network (ANN) simulations used for handwritten
recognition is calculated by fitting LTP/LTD profiles with the following
equation [46]:
(
)
Gn − Gmin
Gn+1 = Gn + αP exp − βP
(for LTP)
(2)
Gmax − Gmin
(
)
Gn − Gmin
Gn+1 = Gn − αP exp − βP
(for LTD)
Gmax − Gmin
(3)
where Gn and Gn+1 are the conductance of the nth and (n+1)th pulses,
Gmin and Gmax are the minimum and maximum conductance, αP and αD
denote the step size of the conductance between two points in the
potentiation and depression curves, and βP and βD represent nonlinearity
of the LTP/LTD curves, respectively. A larger β presents a greater
nonlinearity. For an ideal synaptic device, its weight updating processes
should be linear (β = 0). The nonlinearity of the LTP and LTD for the
Fig. 5. Simulation of neuromorphic computing with parameters of the optical-driven synaptic transistors. (a) LTP/LTD characteristics of ZnSnO nanowire opto­
electronic synapses. (b) Schematic diagram of three-layer artificial neural network. (c) Schematic diagram of an optoelectronic synapse crossbar array for matrix
operations. (d) Recognition accuracy with 40 training epochs for 8 × 8 pixels handwritten digit images. (e) Recognition accuracy with 40 training epochs for 28 × 28
pixels handwritten digit images.
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Materials Science in Semiconductor Processing 158 (2023) 107344
device is calculated to be 0.3 and 2.4, respectively, which can prove the
superior linearity of ZnSnO synaptic transistors. Furthermore, the sym­
metricity is calculated to be 10 with respect to the symmetry of LTP/LTD
characteristics, which is described in Supplementary Note 1. Addition­
ally, the dynamic range of conductance margins (Gmax/Gmin) between
the maximum and minimum conductance values is calculated to be
338.2. Besides, the cyclic stability of the LTP/LTD is also demonstrated
in Fig. S4(a).
To further examine the feasibility of ZnSnO synaptic transistor for
neuromorphic computing, ANN with three layers, one hidden layer, is
used to perform supervised learning on the Modified National Institute
of Standards and Technology (MNIST) database of handwritten digits, as
shown in Fig. 5(b). The numerical weights in neuromorphic computing
are mapped onto the conductance measured in the ZnSnO synaptic
transistors. The weight state is updated by the average change of
conductance from measured LTP/LTD with a sampled noise value from
the corresponding probability distribution. Two matrix operations are
applied to the simulated crossbar based on measured LTP/LTD proper­
ties: a vector matrix multiply, and a parallel rank 1 outer product up­
date, as illustrated in Fig. 5(c) [47]. The conductance states are
programmed by the product of the optical signal along the columns and
the voltage signal along the rows for matrix operations. Aside from
“write” operations, voltages are applied along rows and currents are
measured along columns to read from the crossbar. As a result, for the 8
× 8 pixel small digits, the recognition accuracy obtained by ANN
simulation can reach 95.4%, which is already close to the ideal value of
96.7% (Fig. 5(d)). Fig. 5(e) demonstrates that the accuracy of the device
for large images (28 × 28) is slightly reduced to 92.5%, which is 5.6%
lower than the ideal value. In addition, file types classification accuracy
approaches the ideal numerical training shown in Fig. S4(b). These
simulations demonstrate the potential of optoelectronic synaptic devices
for future applications in neural network computing.
information. To perform real-time image identification, in situ memo­
rizing, and distinction input data, a 5 × 4 pixel information sensingmemory-processing system based on a ZnSnO optical synaptic array is
built here. Fig. S5(a) shows the optical image of a 5 × 4 array of ZnSnO
synaptic transistors, a zoom-in optical view of electrospun nanowires
across the transistor channel is shown in the inset. To verify the uni­
formity of the synaptic array device performance, the transfer charac­
teristic curves of 20 transistors are demonstrated in Fig. S5(b).
Fig. 6(a) depicts the initial state of the optoelectronic synaptic array,
in which all devices are in the off state. Next, the image of the letter “A”
is imported into the optoelectronic synapse array through 100 UV pul­
ses, as shown in Fig. 6(b). The conductance state decays slowly after
removing the UV pulse for 300 s (Fig. 6(c)), which indicates that the
optoelectronic synaptic array implements the memory function. In
addition, Fig. S5(c) shows the retention time of the device current,
demonstrating that the current value of the device has not fully recov­
ered to the initial state after 1.7 × 103 s, which proves that the device
possesses large memory retention. It is worth noting that a negative gate
voltage pulse can be used to erase the memory effect by inducing elec­
tron accumulation. Afterward, another new image of the letter “O” can
be imported after the synaptic array setting to the initial state, as illus­
trated in Fig. 6(e). Similarly, the new input image can still be stored in
the synaptic array for 300 s, and erased to its initial state by adding
negative gate biasing, as shown in Fig. 6(f).
3. Conclusion
Via a low-cost electrospinning coupled with nanowire transfer
technique, we have fabricated UV-driven synaptic transistors based on
ZnSnO nanowires. The UV light with various pulse number and light
intensity was applied to obtain corresponding synaptic signals, such as
EPSCs, PPFs and LTPs. Moreover, EPSCs obtained from ZnSnO nanowire
transistors decay to approximately 93% of the initial value after eight
weeks, indicating superior device environmental stability. Furthermore,
the SnO2/ZnO heterostructures reveal by TEM and XRD measurements
can provide support to effectively mitigate the recombination of UVinduced free carriers in the ZnSnO nanowires. In addition, we have
demonstrated various applications relevant to the UV artificial vision.
2.3.3. Sensing-memory-processing system
Traditional CMOS is usually composed of separate detection, mem­
ory, and processing units, which causes latency in computation and
extra energy loss. In comparison, optoelectronic artificial synapses can
integrate both the sensing of light and the memory and processing of
Fig. 6. Information sensing-memory-processing system with 5 × 4 pixels based on ZnSnO optical synaptic array. (a) Initial state. (b) After 100 UV pulse training
(375 nm, 50 mW cm− 2 for 500 ms with Δt of 500 ms), the letter “A” is input. (c) Letter “A” retention after 300 s. (d) Erase the input image through negative gate
voltage (− 10 V, 3 s). (e) Another image of the letter “O” is input through 100 UV pulse training nm. (f) Letter “O” retention after 300 s.
7
R. Zhou et al.
Materials Science in Semiconductor Processing 158 (2023) 107344
We believe this work can offer opportunity to reveal the importance of
heterostructures in MOS synaptic transistors with superior environ­
mental stability and various applications.
org/10.1016/j.mssp.2023.107344.
4. Experimental section
[1] D. Markovic, A. Mizrahi, D. Querlioz, et al., Physics for neuromorphic computing,
Nat. Rev. Phys. 2 (2020) 499–510, https://doi.org/10.1038/s42254-020-0208-2.
[2] X.H. Liu, F.Y. Wang, J. Su, et al., Bio-inspired 3D artificial neuromorphic circuits,
Adv. Funct. Mater. 32 (2022), 2113050, https://doi.org/10.1002/
adfm.202113050.
[3] B.A. Chen, S.H. Sun, S.Q. Fan, et al., Low-cost fabricated MgSnO electrolyte-gated
synaptic transistor with dual modulation of excitation and inhibition, Adv.
Electron. Mater. 10 (2022), 2200864, https://doi.org/10.1002/aelm.202200864.
[4] H. Ling, D.A. Koutsouras, S. Kazemzadeh, et al., Electrolyte-gated transistors for
synaptic electronics, neuromorphic computing, and adaptable biointerfacing, Appl.
Phys. Rev. 7 (2020), 011307, https://doi.org/10.1063/1.5122249.
[5] Y. Meng, S. Yip, W. Wang, et al., Quantum artificial synapses, Adv. Quantum.
Technol. 4 (2021), 2100072, https://doi.org/10.1002/qute.202100072.
[6] J.R. Yu, G.Y. Gao, J.R. Huang, et al., Contact-electrification-activated artificial
afferents at femtojoule energy, Nat. Commun. 12 (2022) 1581, https://doi.org/
10.1038/s41467-021-21890-1.
[7] S. Zhang, K.X. Guo, L. Sun, et al., Selective release of different neurotransmitters
emulated by a p-i-n junction synaptic transistor for environment-responsive action
control, Adv. Mater. 33 (2021), 2007350, https://doi.org/10.1002/
adma.202007350.
[8] J. Li, W.H. Fu, Y.X. Lei, et al., Oxygen-vacancy-induced synaptic plasticity in an
electrospun InGdO nanofiber transistor for a gas sensory system with a learning
function, ACS Appl. Mater. Interfaces 14 (2022) 8587–8597, https://doi.org/
10.1021/acsami.1c23390.
[9] Q.M. Lian, Y.Q. Liu, X.H. Zhang, et al., Noise detection system based on noise
triboelectric nanogenerator and synaptic transistors, IEEE Electron. Device Lett. 42
(2021) 1334–1337, https://doi.org/10.1109/led.2021.3099510.
[10] T.S. Zhao, C. Zhao, W.Y. Xu, et al., Bio-inspired photoelectric artificial synapse
based on two-dimensional Ti3C2Tx MXenes floating gate, Adv. Funct. Mater. 31
(2021), 2106000, https://doi.org/10.1002/adfm.202106000.
[11] D. Kudithipudi, M. Aguilar-Simon, J. Babb, et al., Biological underpinnings for
lifelong learning machines, Nat. Mach. Intell. 4 (2022) 196–210, https://doi.org/
10.1038/s42256-022-00452-0.
[12] K.P. Tee, S. Cheong, J. Li, et al., A framework for tool cognition in robots without
prior tool learning or observation, Nat. Mach. Intell. 4 (2022) 533–543, https://
doi.org/10.1038/s42256-022-00500-9.
[13] L.L. Gu, S. Poddar, Y.J. Lin, et al., A biomimetic eye with a hemispherical
perovskite nanowire array retina, Nature 581 (2020) 278–282, https://doi.org/
10.1038/s41586-020-2285-x.
[14] J. Dupeyroux, S. Viollet, J.R. Serres, An ant-inspired celestial compass applied to
autonomous outdoor robot navigation, Robot. Autonom. Syst. 117 (2019) 40–56,
https://doi.org/10.1016/j.robot.2019.04.007.
[15] T.H. Goldsmith, Hummingbirds see near ultraviolet light, Science (New York, N.Y.)
207 (1980) 786–788, https://doi.org/10.1126/science.7352290.
[16] M.C. Stoddard, H.N. Eyster, B.G. Hogan, et al., Wild hummingbirds discriminate
nonspectral colors, Proc. Natl. Acad. Sci. U.S.A. 117 (2020) 15112–15122, https://
doi.org/10.1073/pnas.1919377117.
[17] K.S. Edgett, R.A. Yingst, M.A. Ravine, et al., Curiosity’s mars hand lens imager
(MAHLI) investigation, Space Sci. Rev. 170 (2012) 259–317, https://doi.org/
10.1007/s11214-012-9910-4.
[18] T. Wang, C. Schreiber, D. Elbaz, et al., A dominant population of optically invisible
massive galaxies in the early Universe, Nature 572 (2019) 211–214, https://doi.
org/10.1038/s41586-019-1452-4.
[19] Y.X. Hou, Y. Li, Z.C. Zhang, et al., Large-scale and flexible optical synapses for
neuromorphic computing and integrated visible information sensing memory
processing, ACS Nano 15 (2021) 1497–1508, https://doi.org/10.1021/
acsnano.0c08921.
[20] X. Huang, Q.Y. Li, W. Shi, et al., Dual-Mode learning of ambipolar synaptic
phototransistor based on 2D perovskite/organic heterojunction for flexible color
recognizable visual system, Small 17 (2021), 2102820, https://doi.org/10.1002/
smll.202102820.
[21] C. Jo, J. Kim, J.Y. Kwak, et al., Retina-inspired color-cognitive learning via
chromatically controllable mixed quantum dot synaptic transistor arrays, Adv.
Mater. 34 (2022), 2108979, https://doi.org/10.1002/adma.202108979.
[22] Y.R. Wang, F. Wang, Z.X. Wang, et al., Reconfigurable photovoltaic effect for
optoelectronic artificial synapse based on ferroelectric p-n junction, Nano Res. 14
(2021) 4328–4335, https://doi.org/10.1007/s12274-021-3833-x.
[23] M. Divya, J.R. Pradhan, S.S. Priyadarsini, et al., High operation frequency and
strain tolerance of fully printed oxide thin film transistors and circuits on PET
substrates, Small 18 (2022), 2202891, https://doi.org/10.1002/smll.202202891.
[24] W.X. Ouyang, F. Teng, J.H. He, et al., Enhancing the photoelectric performance of
photodetectors based on metal oxide semiconductors by charge-carrier
engineering, Adv. Funct. Mater. 29 (2019), 1807672, https://doi.org/10.1002/
adfm.201807672.
[25] L. Shao, Y. Zhao, Y.Q. Liu, Organic synaptic transistors: the evolutionary path from
memory cells to the application of artificial neural networks, Adv. Funct. Mater. 31
(2021), 2101951, https://doi.org/10.1002/adfm.202101951.
[26] D.J. Li, Y. Meng, Y.N. Zheng, et al., Surface energy-mediated self-catalyzed
CsPbBr3 nanowires for phototransistors, Adv. Electron. Mater. (2022), 22007278,
https://doi.org/10.1002/aelm.202200727.
References
Synthesis of Precursor: The ZnSnO precursor solution was synthesized
based on 0.04 g zinc acetate (Zn(NO3)2•6H2O, 99.99%, Aladdin), 0.07 g
tin chloride dehydrate (SnCl2.4H2O, 99%, Aladdin), and 0.8 g poly­
vinylpyrrolidone (PVP) (Aladdin). The precursors were dissolved in 5 g
of N, N-dimethylformamide (DMF) (99.8%, Aladdin), which were then
magnetically stirred for 12 h at room temperature.
Device Fabrication: Deionized water, acetone, and ethanol were used
sequentially to sonicate Si/SiO2 substrates, then nitrogen was used to
blow dry them. The electrospun ZnSnO nanowires were prepared with a
DC voltage of 16 kV at a flow rate of 8 μl/min. Next, the obtained ZnSnO
nanowires were heated at 150 ◦ C for 10 min, and then exposed under UV
illumination for 40 min to enhance adhesion with the substrate, which
were subsequently subjected to post-annealing at 500 ◦ C for 2 h to
completely remove organic residues. Source/drain electrodes made of
aluminium (Al) with a thickness of 100 nm were eventually patterned
onto the ZnSnO nanowires via thermal evaporation through a shadow
mask (Width/Length = 1000/100 μm).
Material and Device Characterizations: The surface morphology of
nanowires was investigated via SEM (Nova Nano SEM450, 15 kV). The
phase formation and detailed material structure as well as composition
were obtained by XRD (Rigaku D/max-rB), TEM (JEOL JEM 2100F,
operated at 200 kV) together with HRTEM. The electrical signals in
response to optical stimulations were measured using a Keithley B2912A
I–V parametric analyser in ambient condition. An ultraviolet laser (ADR1805) with wavelength of 375 nm coupled with a function generator
(RIGOL DG1022U) were applied as the external light source. The ab­
sorption spectra were obtained with a Shimadzu ultraviolet–visible
spectrophotometer (UV-3600).
CRediT authorship contribution statement
Ruifu Zhou: Software, Investigation, Formal analysis, Data curation,
Conceptualization. Wenxin Zhang: Formal analysis, Data curation.
Haofei Cong: Software, Methodology. Yu Chang: Software, Method­
ology. Fengyun Wang: Writing – review & editing, Project adminis­
tration, Funding acquisition, Conceptualization. Xuhai Liu: Writing –
review & editing, Supervision, Project administration, Funding acqui­
sition, Conceptualization.
Declaration of competing interest
The authors declare that they have no known competing financial
interests or personal relationships that could have appeared to influence
the work reported in this paper.
Data availability
Data will be made available on request.
Acknowledgements
R.F. Zhou and W.X. Zhang contributed equally to this work. All au­
thors have given approval to the final version of the manuscript. This
work was financially supported by the Natural Science Foundation of
Shandong Province, China (ZR2020QF104), Key Research and Devel­
opment Program of Shandong Province, China (2019GGX102067).
Appendix A. Supplementary data
Supplementary data to this article can be found online at https://doi.
8
R. Zhou et al.
Materials Science in Semiconductor Processing 158 (2023) 107344
[38] S.W. Chang, T. Trimbuch, C. Rosenmund, Synaptotagmin-1 drives synchronous
Ca2+-triggered fusion by C2B-domain-mediated synaptic-vesicle-membrane
attachment, Nat. Neurosci. 21 (2018) 33–40, https://doi.org/10.1038/s41593017-0037-5.
[39] C. Han, X.W. Han, J.Y. Han, et al., Light-Stimulated synaptic transistor with high
PPF feature for artificial visual perception system application, Adv. Funct. Mater.
32 (2022), 2113053, https://doi.org/10.1002/adfm.202113053.
[40] S. Song, M. Kim, G. Yoo, et al., Solution-processed oxide semiconductor-based
artificial optoelectronic synapse array for spatiotemporal synaptic integration,
J. Alloys Compd. 857 (2021), 158027, https://doi.org/10.1016/j.
jallcom.2020.158027.
[41] J. Kim, S. Song, H. Kim, et al., Light-stimulated artificial photonic synapses based
on solution-processed In-Sn-Zn-O transistors for neuromorphic applications,
J. Alloys Compd. 903 (2022), 163873, https://doi.org/10.1016/j.
jallcom.2022.163873.
[42] S.W. Cho, D.E. Kim, K.S. Kim, et al., Towards environmentally stable solutionprocessed oxide thin-film transistors: a rare-metal-free oxide-based semiconductor/
insulator heterostructure and chemically stable multi-stacking, J. Mater. Chem. C 5
(2017) 10498–10508, https://doi.org/10.1039/c7tc03393a.
[43] F.Z. Li, Y. Meng, R.T. Dong, et al., High-performance transparent ultraviolet
photodetectors based on InGaZnO superlattice nanowire arrays, ACS Nano 13
(2019) 12042–12051, https://doi.org/10.1021/acsnano.9b06311.
[44] W. Tian, T.Y. Zhai, C. Zhang, et al., Low-cost fully transparent ultraviolet
photodetectors based on electrospun ZnO-SnO2 heterojunction nanofibers, Adv.
Mater. 25 (2013) 4625–4630, https://doi.org/10.1002/adma.201301828.
[45] M. Kumar, S. Abbas, J. Kim, All-oxide-based highly transparent photonic synapse
for neuromorphic computing, ACS Appl. Mater. Interfaces 10 (2018)
34370–34376, https://doi.org/10.1021/acsami.8b10870.
[46] S. Seo, B.S. Kang, J.J. Lee, et al., Artificial van der Waals hybrid synapse and its
application to acoustic pattern recognition, Nat. Commun. 11 (2020) 3936,
https://doi.org/10.1038/s41467-020-17849-3.
[47] E.J. Fuller, F. El Gabaly, F. Leonard, et al., Li-ion synaptic transistor for low power
analog computing, Adv. Mater. 29 (2017), 1604310, https://doi.org/10.1002/
adma.201604310.
[27] L. Liu, B.B. Cui, W.L. Xu, et al., Highly aligned indium zinc oxide nanowire-based
artificial synapses with low-energy consumption, J. Ind. Eng. Chem. 88 (2020)
111–116, https://doi.org/10.1016/j.jiec.2020.03.030.
[28] G. Milano, M. Luebben, Z. Ma, et al., Self-limited single nanowire systems
combining all-in-one memristive and neuromorphic functionalities, Nat. Commun.
9 (2018) 5151, https://doi.org/10.1038/s41467-018-07330-7.
[29] J.Y. He, X.H. Liu, L.F. Song, et al., High annealing stability of InAlZnO nanofiber
field-effect transistors with improved morphology by Al doping, J. Phys. Chem.
Lett. 12 (2021) 1339–1345, https://doi.org/10.1021/acs.jpclett.1c00030.
[30] J.Y. He, P.L. Xu, R.F. Zhou, et al., Combustion synthesized electrospun InZnO
nanowires for ultraviolet photodetectors, Adv. Electron. Mater. 8 (2022), 2100997,
https://doi.org/10.1002/aelm.202100997.
[31] Y. Chang, H.F. Cong, R.F. Zhou, et al., Enhanced artificial synaptic properties
enabled by arrays of electrolyte-gated electrospun InZnO nanowires, Acs Appl.
Electron. Mater. 4 (2022) 2570–2579, https://doi.org/10.1021/acsaelm.2c00326.
[32] T.S. Zhao, C.G. Liu, C. Zhao, et al., High-performance solution-processed Ti3C2Tx
MXene doped ZnSnO thin-film transistors via the formation of a two-dimensional
electron gas, J. Mater. Chem. A. 9 (2021) 17390–17399, https://doi.org/10.1039/
d1ta01355f.
[33] H.C. Zhang, Y. Meng, L.F. Song, et al., High-performance enhancement-mode thinfilm transistors based on Mg-doped In2O3 nanofiber networks, Nano Res. 11
(2018) 1227–1237, https://doi.org/10.1007/s12274-017-1735-8.
[34] W. Xiao, L.B. Shan, H.T. Zhang, et al., High photosensitivity light-controlled planar
ZnO artificial synapse for neuromorphic computing, Nanoscale 13 (2021)
2502–2510, https://doi.org/10.1039/d0nr08082a.
[35] X.Y. Kou, F.Q. Meng, K. Chen, et al., High-performance acetone gas sensor based on
Ru-doped SnO2 nanofibers, Sens. Actuator B-Chem. 320 (2020), 128292, https://
doi.org/10.1016/j.snb.2020.128292.
[36] M. Lee, W. Lee, S. Choi, et al., Brain-inspired photonic neuromorphic devices using
photodynamic amorphous oxide semiconductors and their persistent
photoconductivity, Adv. Mater. 29 (2017), 1700951, https://doi.org/10.1002/
adma.201700951.
[37] R.S. Zucker, W.G. Regehr, Short-term synaptic plasticity, Annu. Rev. Physiol. 64
(2002) 355–405, https://doi.org/10.1146/annurev.physiol.64.092501.114547.
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