ABSTRACT
The core integration and cooperation of the retina, neurons, and synapses in the visual systems enable humans to effectively sense and process visual information with low power consumption. To mimic the human visual system, an artificial sensory nerve, along with optical sensingâa paired-pulse ratio (PPR) of the light pulse stimulated currentsâand neural coding has been developed. For performing the artificial visual perception functions, we consistently reveal the positive and negative correlations between the PPR index and light pulse time interval by applying two consecutive light stimuli with gate voltages of -10 and 5 V, respectively, to a phototransistor. This phototransistor contains a heterostructured channel layer composed of zinc-oxide nanoparticles (ZnO NPs) interconnected with a solution-processed zinc-tin oxide (ZTO) film. The oxygen adsorption and desorption on the ZnO NP surface under light illumination are responsible for the positive-sloped PPR; the electron trapping effect at the ZnO NP/SiO2 interface is attributed to the negative-sloped PPR. The various accountable light power densities and number of surface trap states are considered to be directly realizing these spike-timing interval-dependent characteristics. The actual benefit of these characteristics is the dual temporal coding modes based on multiplicative operation using a ZTO/ZnO NP phototransistor realized via the active gate voltage modulation. The contrary tendency of the PPR index and temporal codingâa major biological neural codingâis well demonstrated by the potential of ZTO/ZnO NP phototransistors to be implemented in sensor networks for an artificial visual perception.
ABSTRACT
Neuromorphic computing, inspired by the biological neuronal system, is a high potential approach to substantially alleviate the cost of computational latency and energy for massive data processing. Artificial synapses with regulable synaptic weights are the basis of neuromorphic computation, providing an efficient and low-power system to overcome the constraints of the von Neumann architecture. Here, we report an ITO/TaOx-based synaptic capacitor and transistor. With the drift motion of mobile-charged ions in the TaOx, the capacitance and channel conductance can be tuned to exhibit synaptic weight modulation. Robust stability in the cycle-to-cycle (C2C) variation is found in capacitance and conductance potentiation/depression weight updating of 0.9 and 1.8%, respectively. Simulation results show a higher classification accuracy of handwritten digit recognition (95%) in capacitance synapses than that in conductance synapses (84%). Besides, the synaptic capacitor consumes much less energy than the synaptic transistor. Moreover, the ITO/TaOx-based capacitor successfully emulates the pain-perceptual sensitization on top of the superior performance, indicating its promising potential in applying the capacitive neural network.
ABSTRACT
Metal oxide ZrO2has been widely explored for resistive switching application due to excellent properties like high ON/OFF ratio, superior data retention, and low operating voltage. However, the conduction mechanism at the atomistic level is still under debate. Therefore, we have performed comprehensive insights into the role of neutral and charged oxygen vacancies in conduction filament (CF) formation and rupture, which are demonstrated using the atomistic simulation based on density functional theory (DFT). Formation energy demonstrated that the fourfold coordinated oxygen vacancy is more stable. In addition, the electronic properties of the defect included supercell confirm the improvement in electrical conductivity due to the presence of additional energy states near Fermi energy. The CF formation and rupture using threefold and fourfold oxygen vacancies are demonstrated through cohesive energy, electron localization function, and band structure. Cohesive energy analysis confirms the cohesive nature of neutral oxygen vacancies while the isolated behavior for +2 charged oxygen vacancies in the CF. In addition, nudged elastic band calculation is also performed to analyze the oxygen vacancy diffusion energy under different paths. Moreover, we have computed the diffusion coefficient and drift velocity of oxygen vacancies to understand the CF. This DFT study described detailed insight into filamentary type resistive switching observed in the experimentally fabricated device. Therefore, this fundamental study provides the platform to explore the switching mechanism of other oxide materials used for memristor device application.
ABSTRACT
In contrast to the commonly present UV light-stimulated synaptic oxide thin-film transistors, this study demonstrates a violet light (wavelength of 405 nm) stimulated zinc-tin oxide (ZTO) photoelectric transistor for potential application in optical neuromorphic computation. Owing to the light-induced oxygen vacancy ionization and persistent photoconductivity effect in ZTO, this device well imitates prominent synaptic functions, including photonic potentiation, electric depression, and short-term memory (STM) to long-term memory (LTM) transition. A highly linear and broad dynamic range of photonic potentiation can be achieved by modulating the light power density, while electric depression is realized by gate voltage pulsing. In addition, the brain-like re-learning experience with extended forgetting time (200 s) is well mimicked by the ZTO photoelectric transistor. As a result, the ZTO photoelectric transistor provides excessive synaptic function with multi-series of synaptic weight levels (90 levels for each given light power density), which makes it prevalent in the neuromorphic computation of massive data as well as in learning-driven artificial intelligence computation.
