Emerging Optoelectronic Devices for Brain‐Inspired Computing

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.

Engineering of TiN/ZnO/SnO2/ZnO/Pt multilayer memristor with advanced electronic synapses and analog switching for neuromorphic computing

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.

Brain‐inspired (neuromorphic) computing that offers lower energy consumption and parallelism (simultaneous processing and memorizing) compared to von Neumann computing provides excellent opportunities in many computational tasks ranging from image recognition to speech processing. To accomplish neuromorphic computing, highly efficient optoelectronic synapses, which can be the building blocks of optoelectronic neuromorphic computers, are necessary. Currently, carbon nanotubes (CNTs), an attractive candidate to develop circuit‐level photonic synapses, provide weak light responses. The inferior photoresponse of CNTs increases the energy consumption of neuromorphic optoelectronic devices. Herein, a method to grow organic–inorganic halide perovskite quantum dots (PQDs) directly on multiwall CNTs (MWCNTs) to increase the photosensitivity of optoelectronic synapses is demonstrated. The new hybrid material synchronizes the high photoresponse of PQDs and the excellent electrical properties of MWCNTs to provide photonic memory under very low light intensity (125 µW cm−2). However, neat MWCNTs do not show any detectable light response at the tested light intensity, as high as 25 mW cm−2. Since the PQDs are grown directly on and in the MWCNTs, the hybrid PQD‐MWCNT provides a new direction for the future MWCNT‐based optoelectronic devices for neuromorphic computing with a potential to break the von Neumann bottleneck.

Graphdiyne (GDY) is emerging as a promising material for various applications owing to its unique structure and fascinating properties. However, the application of GDY in electronics and optoelectronics are still in its infancy, primarily owing to the huge challenge in the synthesis of large-area and uniform GDY film for scalable applications. Here a modified van der Waals epitaxy strategy is proposed to synthesize wafer-scale GDY film with high uniformity and controllable thickness directly on graphene (Gr) surface, providing an ideal platform to construct large-scale GDY/Gr-based optoelectronic synapse array. Essential synaptic behaviors have been realized, and the linear and symmetric conductance-update characteristics facilitate the implementation of neuromorphic computing for image recognition with high accuracy and strong fault tolerance. Logic functions including “NAND” and “NOR” are integrated into the synapse which can be executed in an optical pathway. Moreover, a visible information sensing-memory-processing system is constructed to execute real-time image acquisition, in situ image memorization and distinction tasks, avoiding the time latency and energy consumption caused by data conversion and transmission in conventional visual systems. These results highlight the potential of GDY in applications of neuromorphic computing and artificial visual systems.

Drawing inspiration from biology, neuromorphic systems are of great interest in direct interaction and efficient processing of analogue signals in the real world and could be promising for the development of smart sensors. Here, we demonstrate an artificial sensory neuron consisting of an InGaZnO4 (IGZO4)-based optical sensor and NbOx-based oscillation neuron in series, which can simultaneously sense the optical information even beyond the visible light region and encode them into electrical impulses. Such artificial vision sensory neurons can convey visual information in a parallel manner analogous to biological vision systems, and the output spikes can be effectively processed by a pulse coupled neural network, demonstrating the capability of image segmentation out of a complex background. This study could facilitate the construction of artificial visual systems and pave the way for the development of light-driven neurorobotics, bioinspired optoelectronics, and neuromorphic computing.

Being capable of dealing with both electrical signals and light, artificial optoelectronic synapses are considered to be an important cornerstone of neuromorphic computing. Here, an artificial optoelectronic synapse is reported through a simple solution process using organic poly(3‐hexylthiophene) (P3HT) and a remarkable analog switching characteristic similar to synaptic behavior is observed. The endurance and data retention capability of the P3HT‐based optoelectronic synapse exhibit stable characteristics up to 5000 consecutive cycles and 104 s. Through in‐depth physical mechanism analysis, it is confirmed that the analog switching characteristics of the device are mainly caused by a tunneling mechanism and space charge limited conduction. Furthermore, characterizations such as X‐ray photoelectron spectroscopy and atomic layer deposition prove that the memristive properties of device can be attributed to ion migration. More importantly, the device can co‐modulate the optoelectronic signal and successfully implement a photo‐triggered multi‐signal mode response. Based on this, a 3 × 3 synapse array is developed to demonstrate the potential application of the proposed P3HT‐based optoelectronic synapse in constructing an artificial visual system. Finally, operant conditioning is successfully simulated in the synaptic device. This work provides a reference for the construction of optoelectronic synapses in the neuromorphic visual system.

Emulating synaptic functionalities in optoelectronic devices is significant in developing artificial visual-perception systems and neuromorphic photonic computing. Persistent photoconductivity (PPC) in metal oxides provides a facile way to realize the optoelectronic synaptic devices, but the PPC performance is often limited due to the oxygen vacancy defects that release excess conduction electrons without external stimuli. Herein, a high-performance optoelectronic synapse based on the stoichiometry-controlled LaAlO3/SrTiO3 (LAO/STO) heterostructure is developed. By increasing La/Al ratio up to 1.057:1, the PPC is effectively enhanced but suppressed the background conductivity at the LAO/STO interface, achieving strong synaptic behaviors. The spectral noise analyses reveal that the synaptic behaviors are attributed to the cation-related point defects and their charge compensation mechanism near the LAO/STO interface. The short-term and long-term plasticity is demonstrated, including the paired-pulse facilitation, in the La-rich LAO/STO device upon exposure to UV light pulses. As proof of concepts, two essential synaptic functionalities, the pulse-number-dependent plasticity and the self-noise cancellation, are emulated using the 5×5 array of La-rich LAO/STO synapses. Beyond the typical oxygen deficiency control, the results show how harnessing the cation stoichiometry can be used to design oxide heterostructures for advanced optoelectronic synapses and neuromorphic applications.

Artificial neural network and neuron models have made significant contributions to the area of neurodynamics. Investigating the dynamics of artificial neurons and neural networks is vital in developing brain-like systems and understanding how the brain functions. Neural network models and memristive neurons are currently demonstrating a lot of promise in the study of neurodynamics. In order to model the dynamics of biological synapses, this study explores the complex dynamical behavior of a discrete fractional Hopfield-type neural network using a flux-controlled memristive element with periodic memductance. Hyperbolic tangent and sine are the heterogeneous activation functions that are implemented in the proposed system to improve nonlinearity and replicate various forms of brain activity. Stability and bifurcation analyses are used to illustrate the nonlinear dynamical nature of the constructed network model. We examine how the fractional order (ν) and periodical memductance aspects influence the dynamics of the system to emphasize the emerging complex phenomena like multi-layered dynamics and the presence of several distinct dynamical states throughout the system variables. Randomness and complexity of the time series data for the proposed system are illustrated with the help of approximate entropy analysis. These findings could help researchers better understand brain-like memory networks, neuromorphic computers, and the theoretical study of neurological and mental abilities. The study of multi-layer attractors can be useful in advanced sensory devices, neuromorphic devices, and secure communication.

Simulating the human brain for neuromorphic computing has attractive prospects in the field of artificial intelligence. Optoelectronic synapses have been considered to be important cornerstones of neuromorphic computing due to their ability to process optoelectronic input signals intelligently. In this work, optoelectronic synapses based on all‐inorganic perovskite nanoplates are fabricated, and the electronic and photonic synaptic plasticity is investigated. Versatile synaptic functions of the nervous system, including paired‐pulse facilitation, short‐term plasticity, long‐term plasticity, transition from short‐ to long‐term memory, and learning‐experience behavior, are successfully emulated. Furthermore, the synapses exhibit a unique memory backtracking function that can extract historical optoelectronic information. This work could be conducive to the development of artificial intelligence and inspire more research on optoelectronic synapses.

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

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.

Halide perovskites are considered as promising memristive materials for next‐generation optoelectronic devices. This review concisely summarizes the recent development of halide perovskite memristors and highlights their advancements in optoelectronic applications: light‐induced low power switches, optoelectronic logic operations, optoelectronic neuromorphic computation, and artificial vision systems. Finally, we address the challenges and future development prospects of halide perovskites‐based memristors. This review highlights the promising potential of halide perovskite materials for future optoelectronic memory and computing applications.

In this work, achieving a critical balance between defect activity and lattice stability is demonstrated in Sn-doped oxides, leveraging Sn’s high ionization potential. Optimizing the Sn concentration to 8.6 at. % enhances the distribution and concentration of oxygen vacancies, significantly improving the performance of indium zinc tin oxide (IZTO) neuromorphic devices. The devices exhibit superior bias stability and enhanced memory characteristics under optical pulses, with a 26% increase in photocurrent retention. Uniform oxygen vacancies enable persistent photoconductivity-driven facilitation and memory transitions, mimicking neuromorphic preprocessing functions such as high-pass filtering for visual edge enhancement. Furthermore, mapping the conductive state evolution to a reservoir computing framework achieves 87% accuracy in clothing category classification, demonstrating robustness under noise. This work establishes high-ionization-potential element doping as a strategic approach to stabilize optoelectronic neuromorphic devices and advance neuromorphic computing-hardware integration.

Neuromorphic computing has gained a significant amount of attention from industry as well as the research community as a means of overcoming the growing energy demands of machine intelligence. Neuromorphic systems offer promising computation approaches by mimicking the functioning of the human brain’s energy-efficient neural processing. This perspective explores the significance of energy efficiency in artificial intelligence (AI) systems, examines the potential issues associated with traditional AI architectures, and highlights the importance of neuromorphic computing as a sustainable solution. It also provides an overview of the working principle of neuromorphic systems, a spiking neural network (SNN) implementation, and the comparative advantages over conventional computing architectures. This perspective further investigates several real-world applications to assess the potential of neuromorphic computing to perform real-time and energy-efficient operations. Although they offer significant advantages, neuromorphic systems face multiple challenges regarding materials development, fabrication techniques, large-scale deployments, and a lack of standard software tools. Finally, this perspective outlines potential research directions to explore the role of neuromorphic computing in next-generation AI systems.