Self‐Powered Optoelectronic Synaptic Devices Based on In2Se3/MoS2 Ferroelectric Heterojunction with Boosted Performance

Zero-power optoelectronic synaptic devices

Simulating biological synaptic functionalities through artificial synaptic devices opens up an innovative way to overcome the von Neumann bottleneck at the device level. Artificial optoelectronic synapses provide a non‐contact method to operate the devices and overcome the shortcomings of electrical synaptic devices. With the advantages of high photoelectric conversion efficiency, adjustable light absorption coefficient, and broad spectral range, nanowires (NWs)‐based optoelectronic synapses have attracted wide attention. Herein, to better promote the applications of nanowires‐based optoelectronic synapses for future neuromorphic systems, the functionalities of optoelectronic synaptic devices and the current progress of NWs optoelectronic synaptic devices in UV–vis–IR spectral range are introduced. Furthermore, a bridge between NWs‐based optoelectronic synaptic device and the neuromorphic system is established. Challenges for the forthcoming development of NWs optoelectronic synapses are also discussed. This review may offer a vision into the design and neuromorphic applications of NWs‐based optoelectronic synaptic devices.

Synaptic devices based neuromorphic computing applications in artificial intelligence

To overcome the coming big-data era, significant efforts are needed to implement neuromorphic computing that mimics the functional and structural features of the human brain using electronic devices. Repeated updates mimicking biological synaptic plasticity degrade the endurance of synaptic devices during in situ training. This study demonstrates a two-dimensional (2D) ferroelectric α-In2Se3-based synaptic device with enhanced durability via self-curing performed from current annealing (CA) in synaptic fatigue occurring during repetitive learning. The conduction, degradation, and self-curing mechanisms of the 2D ferroelectric-based synaptic device are quantitatively elucidated by low-frequency noise (LFN) spectroscopy. The classification accuracy of the Canadian Institute for Advanced Research (CIFAR)-10 dataset with self-cured conductance is superior to that of the device with synaptic fatigue and recovers to the initial accuracy level. The simulation results of removing defect cells through self-curing in the 2D ferroelectric synaptic array can help reduce energy consumption in the long term. The experimental results emphasize adopting 2D ferroelectric materials for future neuromorphic computing.

As Moore's law approaches its limits, it is gradually harder to achieve the more and more complex tasks with computers with conventional von Neumann architecture. The hardware circuits for neuromorphic computing, with low power and high computing density, have attracted a lot of attention. Based on these requirements, how to choose the appropriate circuit components for neuromorphic computing becomes very important. Ambipolar synaptic devices with tunable and versatile behaviours, as a new type of circuit component, can mimic both excitatory and inhibitory behaviours, which has great potential in neuromorphic computing. In this chapter, we will introduce the definitions of ambipolar materials and synaptic devices. Then, the characteristics and applications of the synaptic devices will be discussed. The differences between conventional and ambipolar synaptic devices will also be compared. Finally, we will introduce the possibility of applying ambipolar synaptic devices in array to achieve more complex work.

Artificial neural networks built with optoelectronic synaptic devices have been proven to process visual information effectively. However, it takes great latency time and energy consumption, especially facing dynamic visual information, due to the separated optical sensor, memory, and process unit. Reservoir computing (RC) based on optoelectronic synaptic devices provides an in-sensor RC for processing temporal information efficiently. It achieves efficient computation by sensing and processing optical signals directly with optoelectronic synaptic devices. Optoelectronic synaptic devices shine in visual information processing, whose application in visual sensing and processing will provide a viable hardware solution for in-sensor computing. Therefore, the application of optoelectronic synaptic devices in reservoir computing has prompted increasing attention. Herein, for promoting the application of physical reservoir computing (PRC) with optoelectrical synapses in machine vision, synaptic plasticity will be introduced first in this work and then illustrate the basic functions of optoelectronic synapses as well as their application in reservoir computing further, and provide a perspective on PRC with optoelectronic synapses in the final.

Optoelectronic synaptic devices outperform electrical synapses in speed, energy efficiency, and integration density. Recent progress in visual sensing and optogenetics has led to the integration of light-sensitive materials in these devices, promising unmatched speed, connectivity, and bandwidth. Here, we present a copper phthalocyanine (CuPc) based optoelectronic synaptic device boasting femto Joule power consumption stable at room temperature. The optoelectronic synapse can be operated with energy consumption as low as 430.4 fJ which is very attractive from the point of view of low-power neuromorphic devices. By modulating light pulses, the neuromorphic behavior can be emulated including excitatory post-synaptic current (EPSC), paired-pulse facilitation (PPF), transitioning from short-term plasticity (STP) to long-term plasticity (LTP), spike-rate dependent plasticity (SRDP) and spike-number dependent plasticity (SNDP), etc. Optical potentiation and electrical depression are observed with combined optical and electrical stimulation, proving the multi-functionality of the synapse. Furthermore, the device demonstrates classical associative learning behaviors like Pavlovian conditioning using optical and electrical stimuli. We have established the pain conditioning processes such as hyperalgesic response and pain extinction effects with varying optical pulse amplitudes. These results render the CuPc-based devices as multifunctional and highly versatile artificial synaptic devices for future computing applications, offering unprecedented efficiency and functionality in neuromorphic systems.

An oxide-based heterojunction optoelectronic synaptic device with wideband and rapid response performance

Synapse is one of the key components of brain nervous system. As the fourth electronic component, memristor can be used to act as synaptic device and construct bionic neural network, in which the synapses can be stimulated by either electrical excitation or optical irradiation. Up to now, there have been many researches on the optoelectronic synaptic devices and the main functional materials include binary oxides, perovskite complex oxides, solid electrolytes, organic and polymer materials, etc. Here, we report anoptoelectronic synaptic device based on SnO₂monolayer embedded with Ag nanocrystals and structured as ITO/SnO₂:Ag/p++-Si. It is found that the SnO2:Ag thin film can effectively absorb light illumination in the broadband range of 450 nm to 635 nm, and that the device has been successfully simulated with multiple synaptic behaviors in the broadband range, including excitatory postsynaptic current (EPSC), paired pulse facilitation (PPF) and the transition from short-term plasticity (STP) to long-term plasticity (LTP). Our newly developed optoelectronic synapse device shows great application potential in neuromorphological computing.

Optoelectronic synapses use optical signals as modulation inputs and offer advantages such as low-power consumption, non-contact operation, and high parallelism. However, devices operating in the visible and near-infrared regions are susceptible to ambient light interference, leading to signal distortion and high background noise. In contrast, optoelectronic synaptic devices working in a solar-blind region are impervious to external ambient interference, enabling them to achieve stable and high-fidelity signal transmission effectively. Ga2O3 as a wide bandgap with inherent oxygen-vacancy defects has strong deep-ultraviolet light sensitivity, making it an ideal material for solar-blind optoelectronic synapses. Here, high-performance solar-blind synaptic devices were prepared using nanowire network interfacial effects and nitrogen doping to modulate oxygen vacancies. Notably, the paired-pulse facilitation index increased by approximately 1.2 fold after doping. In handwritten digit recognition experiments, the recognition accuracy of the doped device exceeded 90%, representing an improvement of nearly 15% compared to the undoped counterpart. Furthermore, leveraging its enhanced light perception and strong memory characteristics, a precise optical encryption communication scheme with strong anti-interference capability was also designed. The optoelectronic synaptic devices based on element regulated Ga2O3 nanowire network provide a new way for the development of the next generation of high-performance solar-blind optoelectronic neuromorphic systems.

In this paper, gradual and symmetrical long-term potentiation (LTP) and long-term depression (LTD) were achieved by applying the optimal electrical pulse condition of the interfacial phase-change memory (iPCM) based on a superlattice (SL) structure fabricated by stacking GeTe/Sb2Te3 alternately to implement an artificial synapse in neuromorphic computing. Furthermore, conventional phase-change random access memory (PCRAM) based on a Ge–Sb–Te (GST) alloy with an identical bottom electrode contact size was fabricated to compare the electrical characteristics. The results showed a reduction in the reset energy consumption of the GeTe/Sb2Te3 (GT/ST) iPCM by more than 69% of the GST alloy for each bottom electrode contact size. Additionally, the GT/ST iPCM achieved gradual conductance tuning and 90.6% symmetry between LTP and LTD with a relatively unsophisticated pulse scheme. Based on the above results, GT/ST iPCM is anticipated to be exploitable as a synaptic device used for brain-inspired computing and to be utilized for next-generation non-volatile memory.

Energy intensity is one of the most important energy feature that has a dramatic value in energy system of Iran. Indeed, Iran is one of the most energy intensive countries in the world and its main reason is related to high energy consumption in household section. In this article, we present a non-linear model that considers three scenarios in management of household energy demand reducing. Therefore, a rational percentage of energy consumption reduction in the household sector, which firstly eliminates the imbalance between energy production and consumption, and secondly, derives a rational amount of profit from various reduction scenarios, will be presented. The mentioned rational profits in this article are obtained from three scenarios. In the first scenario, it is assumed that the percentage reduction in household energy consumption will be allocated to reducing energy demand in the industrial sector, resulting in profits from value-added creation in this sector. In the second scenario, all benefits from reducing energy consumption in the household sector will be devoted to energy exports, yielding profits from this source. Finally, in the third scenario, the reduction in energy consumption will lead to a decrease in energy supply and consequently a reduction in energy supply costs. To conduct a comprehensive study, a combination of the mentioned scenarios has also been modeled and investigated. The model results indicate that with a 25% reduction in household energy consumption in the 2024-2034 timeframe, the energy imbalance will be eliminated, and allocating 5% of this reduction entirely to the industrial sector will result in profits equivalent to $164.18 billion. However, it should be noted that in the considered combined scenarios, the greater the share of the first scenario, the higher the resulting profit, and the optimal point is achieved in the first scenario.

Nonvolatile memory (NVM)‐based neuromorphic computing has been attracting considerable attention from academia and the industry. Although it is not completely successful yet, remarkable achievements have been reported pertaining to synaptic devices that can leverage NVM capable of storing multiple states. The analog synaptic devices performing computation similar to biological nerve systems are crucial in energy‐efficient analog neuromorphic computing systems. To use NVM as an analog synaptic device, researchers focus on improving device characteristics. Among various characteristics, the most challenging one is linearity and symmetry of synaptic weight update that is required for on‐chip training. In this regard, this review paper discusses recent synaptic device improvements focusing on novel schemes tailored for each NVM device to improve the linearity and symmetry. In addition to device‐level studies, recent research achievements are reviewed expanded up to chip‐level studies because in realizing neuromorphic hardware systems beyond a single synaptic device, several considerations and requirements are needed to confirm for high‐level design, and accordingly, cooptimize among synaptic devices, synapse arrays, electrical circuits, neural networks, algorithms, and implementation. Also, this review introduces various circuit and algorithmic approaches to compensate for the non‐ideality of the analog synaptic device.

Filamentary and interface switching of CMOS-compatible Ta2O5 memristor for non-volatile memory and synaptic devices

Developing an artificial visual sensory system requires optoelectronic materials and devices that can mimic the behavior of biological synapses. Organic/polymeric semiconductors have emerged as promising candidates for optoelectronic synapses due to their tunable optoelectronic properties, mechanic flexibility, and biological compatibility. In this review, we discuss the recent progress in organic optoelectronic synaptic materials and devices, including their design principles, working mechanisms, and applications. We also highlight the challenges and opportunities in this field and provide insights into potential applications of these materials and devices in next‐generation artificial visual systems. By leveraging the advances in organic optoelectronic materials and devices, we can envision its future development in artificial intelligence.