In-sensor reservoir computing based on optoelectronic synaptic devices

Silicon- (Si-) based optoelectronic synaptic devices mimicking biological synaptic functionalities may be critical to the development of large-scale integrated optoelectronic artificial neural networks. As a type of important Si materials, Si nanocrystals (NCs) have been successfully employed to fabricate optoelectronic synaptic devices. In this work, organometal halide perovskite with excellent optical asborption is employed to improve the performance of optically stimulated Si-NC-based optoelectronic synaptic devices. The improvement is evidenced by the increased optical sensitivity and decreased electrical energy consumption of the devices. It is found that the current simulation of biological synaptic plasticity is essentially enabled by photogating, which is based on the heterojuction between Si NCs and organometal halide perovskite. By using the synaptic plasticity, we have simulated the well-known biased and correlated random-walk (BCRW) learning.

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.

Silicon (Si) is one of the most important materials for very large-scale integration (VLSI) circuits, which has achieved great success in microelectronics. The advanced mature technology and the low cost of Si have attracted interest for exploring its use in optoelectronic synaptic devices. Si-based heterostructures with rationally designed energy-band alignments pave the way to optoelectronic synaptic devices with a series of advantages such as higher optical sensitivity and lower energy consumption. In this work, we introduce the use of Si-based heterostructures to develop optoelectronic synaptic devices that are based on bulk Si, Si nanomembranes (Si NMs) and Si nanocrystals (Si NCs). The working mechanisms and existing practical applications of these devices are discussed in terms of energy-band alignment. The continuous exploration of Si-based heterostructures contributes to the optoelectronic implementation of neuromorphic systems in the future.

As a new generation of core components for neuromorphic computing, optoelectronic synaptic devices have the advantages of high bandwidth and less energy consumption. They can combine the functions of visual sensing, signal processing, and memory in one device. Herein, an all‐solid‐state optoelectronic synaptic transistor using indium gallium zinc oxide (IGZO) as the channel and lanthanum fluoride (LaF3) as the gate dielectric is designed and fabricated. Its synaptic plasticity induced by electric and optical stimuli is investigated. LaF3 is a solid superionic conductor with plenty of mobile fluoride ions, which can be used to achieve a high on/off ratio of more than 105 and a low subthreshold swing less than 150 mV dec−1 in this novel synaptic transistor. Due to the ion‐trapping effect at the interface, synaptic plasticity can be achieved upon electrical stimulation. Synaptic plasticity by light pulse stimuli can also be accomplished with the assistance of the persistent photoconductivity effect in IGZO channel. Furthermore, it is found that the synaptic plasticity induced by light stimuli can be modulated by gate voltage. These results provide a new way to control synaptic function and extend the function of optoelectronic synaptic devices.

Engineering semiconductor heterojunctions is a pivotal research frontier for developing next‐generation optoelectronic devices, particularly for neuromorphic computing. However, performing precise, stimulus‐selective control over interfacial carrier dynamics to unlock advanced memory and logic functions remains a significant challenge. Herein, we unveil a novel “photon‐energy‐gated selective carrier accumulation” mechanism within macroscopically ordered ZnO/Ga 2 O 3 core–shell nanowire heterojunctions, governed by the Type‐I band alignment at the heterointerface. This unique architecture, fabricated via a super‐aligned carbon nanotube template, not only boosts the UV photoresponsivity by 78‐fold compared to pure ZnO counterparts but also enables a remarkable wavelength‐selective activation of persistent photoconductivity. Harnessing this optically switchable memory effect, we demonstrate its application in optoelectronic synaptic devices, and we also prove its potential in physical reservoir computing system, achieving a high classification accuracy of 87.9% on the Fashion ‐ Modified National Institute of Standards and Technology (F‐MNIST) dataset. This study provides a new paradigm for designing intelligent optoelectronic devices by precisely manipulating interfacial carrier dynamics, opening promising avenues for in‐sensor computing and advanced photonic memory.

Physical reservoir computing utilizes a physical system as a computational resource. This nontraditional computing technique can be computationally powerful, without the need of costly training. Here, a Hopf oscillator is implemented as a reservoir computer by using a node-based architecture; however, this implementation does not use delayed feedback lines. This reservoir computer is still powerful, but it is considerably simpler and cheaper to implement as a physical Hopf oscillator. A non-periodic stochastic masking procedure is applied for this reservoir computer following the time multiplexing method. Due to the presence of noise, the Euler–Maruyama method is used to simulate the resulting stochastic differential equations that represent this reservoir computer. An analog electrical circuit is built to implement this Hopf oscillator reservoir computer experimentally. The information processing capability was tested numerically and experimentally by performing logical tasks, emulation tasks, and time series prediction tasks. This reservoir computer has several attractive features, including a simple design that is easy to implement, noise robustness, and a high computational ability for many different benchmark tasks. Since limit cycle oscillators model many physical systems, this architecture could be relatively easily applied in many contexts.

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

Zero-power optoelectronic synaptic devices

Physical reservoir computing, which is a promising method for the implementation of highly efficient artificial intelligence devices, requires a physical system with nonlinearity, fading memory, and the ability to map in high dimensions. Although it is expected that spin wave interference can perform as highly efficient reservoir computing in some micromagnetic simulations, there has been no experimental verification to date. Herein, reservoir computing is demonstrated that utilizes multidetected nonlinear spin wave interference in an yttrium‐iron‐garnet single crystal. The subject computing system achieves excellent performance when used for hand‐written digit recognition, second‐order nonlinear dynamical tasks, and nonlinear autoregressive moving average (NARMA). It is of particular note that normalized mean square errors for NARMA2 and second‐order nonlinear dynamical tasks are 1.81 × 10−2 and 8.37 × 10−5, respectively, which are the lowest figures for any experimental physical reservoir so far reported. Said high performance is achieved with higher nonlinearity and the large memory capacity of interfered spin wave multidetection.

Current artificial neuromorphic systems are mainly constructed using solid‐state optoelectronic synaptic devices, limiting their potential artificial intelligence applications in liquid medium. Here, colloidal CuInGaSe/ZnSe quantum dots (QDs) with environment‐benign feature and broad visible light absorption are prepared to fabricate a photoelectrochemical (PEC)‐type optoelectronic synaptic device operating in aqueous electrolyte, enabling unique biological synaptic behaviors including paired‐pulse facilitation (PPF), short‐term plasticity (STP), long‐term plasticity (LTP) and learning‐forgetting‐relearning process when simulated by optical pulses with various wavelength, pulse number, frequency, power density, and applied voltage. These results demonstrate the feasibility of building PEC‐type optoelectronic synapses using colloidal QDs, paving the way to realize available optoelectronic synaptic devices for prospective underwater artificial intelligence technology.

Physical reservoir computing is a computational paradigm that enables spatiotemporal pattern recognition to be performed directly in matter. The use of physical matter leads the way toward energy‐efficient devices capable of solving machine learning problems without having to build a system of millions of interconnected neurons. Proposed herein is a high‐performance “skyrmion mixture reservoir” that implements the reservoir computing model with multidimensional inputs. This implementation solves spoken digit classification tasks with an overall model accuracy of 97.4% and a < 1% word error ratethe best performance ever reported for in materio reservoir computers. Due to the quality of the results and the low‐power properties of magnetic texture reservoirs, it is evident that skyrmion fabrics are a compelling candidate for reservoir computing.

With the growing use of artificial neural networks (ANNs) in temporal data processing tasks, the cost of training for complex ANNs is an escalating concern. Physical reservoir computing (RC), a variation of recurrent neural networks (RNNs), obviate the need for most data intensive matrix vector multiplication in the recurrent layer by evolving the RC’s internal states with the inherent nonlinear dynamics and short-term memory. In this study, we show that magnetic skyrmion confined in a fixed geometry forming the soft layer of a magnetic tunnel junction (MTJ) can work as a RC and perform autonomous long-term prediction of temporal data. Our proposed skyrmion reservoir allows for manipulation of spin dynamics with ultra energy efficient voltage controlled magnetic anisotropy modulation (VCMA) method. Furthermore, the boundary effect on the skyrmion from the geometric edges provides necessary consistency property of the reservoir. We employ our proposed reservoir for the modeling and prediction of the chaotic time series such as Mackey-Glass and dynamic time-series data, such as household building energy loads. For autonomous run, the predicted output is fed to the input of the reservoir. By comparing our spintronic physical RC approach with energy load forecasting algorithms, such as LSTMs and RNNs, we conclude that the proposed framework presents good performance in achieving high predictions accuracy, while also requiring low memory and energy both of which are at a premium in hardware resource and power constrained edge applications. Further, the proposed approach is shown to require very small training datasets and at the same time being at least 16× energy efficient compared to the sequence to sequence LSTM for accurate household load predictions. Higher endurance, fast processing and well-established technology (i.e., MTJ) to integrate with CMOS technology makes such spintronic reservoir attractive over other emergent memory device-based reservoirs.

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.

It is known that it is possible to develop a chemically intelligent robot that can perceive spatial extent using the BZ reaction. If the BZ reaction can be used for learning, there is a possibility of developing a chemically intelligent robot that can make complex judgments about situations like living organisms. In the physical reservoir calculation, it is possible to construct a learning system using a material system, so in this study, we build a physical reservoir calculation using the BZ reaction and confirmed that learning is possible. The physical reservoir computation was capable of performing tasks such as time series prediction and speech recognition. BZ reaction, nonlinear chemical reaction, reservoir computing, time series prediction, speech recognition

Optoelectronic synaptic devices are of great scientific and practical importance because of various potential applications such as ocular simulating and optical-electrical managers based on a new optoelectronic coupling mechanism. In this work, we design a novel channel layer with p-type CsPbBr3 nanoparticles (NPs) buried in an InGaZnO (IGZO) film to construct the corresponding thin-film transistors (TFTs), which exhibits intense improvement in visible-light photosensitivity and synaptic plasticity as compared to the pure IGZO counterpart. Specifically, the composite device is able to exhibit versatile synaptic behavior under light stimuli with density as low as 0.12 μW/cm2 and with the gain 5-20 times higher than that of the IGZO TFT in the visible-light region. Based on the band alignment between the IGZO and NPs, the excitation and decay processes of intrinsic and photoinduced carriers are discussed. Moreover, owing to the gate bias control in a three-terminal configuration, our TFT synapses can imitate complex biological behaviors including the famous "Pavlov's dog" experiment and the "reward and punishment mechanism" of the brain via editing the gate voltage/light pulse stimuli.