Xi Chen

Reservoir computing bridges the gap between artificial intelligence and the physical environment. The realization of such systems depends on artificial synapses that emulate neurobiological plasticity in response to external stimuli. Functioning as reservoirs, these synapses convert the input signals into reservoir states, which are then retrieved by the readout layer. However, energy is merely dissipated during the operation of existing artificial synapses. Herein, we propose an energy-storage-involved reservoir architecture based on graphene-based supercapacitors. Reduced graphene oxide electrode, fabricated via metal-assisted microwave reduction, exhibits a mesoporous structure and low defect density. The supercapacitor delivers a specific capacitance of 352 F g–1 at a current density of 1 A g–1. Under current stimuli, the energy-storage device replicates biological synaptic functions through voltage responses. Next, the voltage responses can be used to mimic human perceptual learning, in which sensory systems are enhanced through experience. Moreover, integrated with a diffractive deep neural network in simulation, supercapacitor-based reservoir preprocessing can accurately identify static and dynamic handwritten images, outperforming an artificial neural network and an unprocessed diffractive deep neural network. These results pave the way for neuromorphic computing that integrates energy-storage-involved information processing, offering promising applications for energy-efficient artificial intelligence hardware.

In‐sensor computing integrates signal acquisition and processing within a single device, enabling real‐time, low‐power visual perception. Wavelength‐multiplexing holds great promise for leveraging the spectral dimension of light to expand parallel processing channels. However, current in‐sensor architectures typically rely on photodetectors with fixed spectral responses, limiting their ability to perform wavelength‐specific computations. Here, we present a neuromorphic wavelength photodetector integrated with 3D laser‐nanoprinted nanopillar metafilters that can simultaneously detect light intensity and wavelength across the visible range. The device features a bidirectional Indium Tin Oxide/n‐Si/Indium Tin Oxide heterojunction with two independently addressable regions, each covered by a metafilter with distinct transmittance spectra. By analyzing the photocurrent ratio under opposite biases, wavelength‐resolved detection is achieved with a resolution of 16.9 nm after accounting for the power‐dependent variation of the photocurrent ratio. Furthermore, the metafilters enable wavelength‐dependent binary weight encoding, thus we could demonstrate a binary neural network (BNN) model that switches computational tasks based on input wavelength—classifying handwritten digits at 520 nm and fashion items at 450 nm, respectively. This work provides a scalable and programmable approach for multifunctional, wavelength‐multiplexed in‐sensor computing, and highlights the potential of laser‐printed metasurface‐integrated photodetectors for future intelligent optoelectronics.

Optical neuromorphic computing offers advantages such as high speed, parallel processing, and strong interference resistance compared with conventional electronic computing methods. However, electrical signals cannot be used as inputs to optical neuromorphic computing frameworks. Optical neuromorphic computing faces a tremendous challenge in converting electrical signals into optical responses. In this paper, a pathway for the conversion based on artificial electrochromic synapses is proposed. The synapse, comprising manganese dioxide and tungsten oxide, exhibits tunable optical transmittance under varying second-level voltage pulses. Based on the transmittance responses, typical behaviors, such as short-term/long-term memory transition and paired-pulse facilitation, can be observed. Moreover, image recognition based on artificial electrochromic synapses can mimic an adaptation effect to accelerate recognition speed and enhance energy efficiency. The responses of recognition accuracy changes turn weak gradually through voltage stimuli after repetitive exposures. The results imply that integrating artificial electrochromic synapses with optical neuromorphic computing offers a new approach to realizing low-cost, fast, and efficient artificial intelligence systems.

Conventional optoelectronic synapses rely on electrical signals for core operations, resulting in complex circuitry, limited response speed, and energy inefficiency. Herein, an all-optical synapse based on perovskite MAPbBr₂I is developed that directly converts optical stimuli into transmittance responses that mimic fundamental synaptic plasticity, including paired-pulse facilitation, short- and long-term memory, and learning. By using the dynamic transmittance response as input to an artificial neural network, high-accuracy dynamic pattern recognition of sequential characters is achieved. Furthermore, the optically controlled transmittance states are successfully integrated as programmable weights into a diffractive neural network, enabling all-optical classification of MNIST handwritten digits with an accuracy of 89%. This fully optical architecture, which eliminates electronic components and complex circuits, offers a promising pathway toward high-speed, energy-efficient vision systems by fundamentally circumventing the von Neumann bottleneck.

Two‐terminal optoelectronic synapses have garnered great attention in neuromorphic computing due to their advantages in structural simplicity and energy efficiency. However, the device's operational flexibility is fundamentally limited by its spectral and electrical characteristics, which fail to tune photoresponses with wavelength selectivity and voltage modulation. Here, a MoS ₂ /Au nanoparticle two‐terminal optoelectronic synapse is presented that simultaneously achieves wavelength selectivity and voltage‐modulated memory control. Adjusting the morphology of Au nanoparticles enables selective maximum photoresponses across the green and red wavelength regions, and voltage modulation induces switching between short‐term and long‐term memory states, thereby enhancing functional versatility. Based on this tunable photoresponse of the synapse, wavelength‐selective image denoising and recognition tasks are demonstrated, achieving a maximum classification accuracy of 98.03% at 520 nm across the visible wavelength range. A wavelength‐selective and voltage‐modulated task for road condition classification is also implemented, incorporating an artificial neural network to track color‐coded vehicles and classify their movement trajectories with precise directional recognition (96.67% accuracy). This work demonstrates a highly tunable optoelectronic synapse that achieves concurrent wavelength selectivity and voltage modulation in a single device, enabling neuromorphic computing and providing a practical strategy for developing highly efficient, multifunctional neuromorphic architectures.

Breaking the optical diffraction limit is essential for advancing nanotechnology. This perspective discusses the advances of dual-beam nanoscale fabrication strategies, inspired by stimulated emission depletion microscopy, in enabling next-generation optical data storage and neuromorphic computing. It introduces the synergistic use of excitation and inhibition laser beams to enable super-resolution lithography, surpassing the diffraction limit to achieve feature sizes below 20 nm in photoresists and 90 nm in laser-scribed graphene. Key breakthroughs include petabit-capacity 3-dimensional optical disk memory using aggregation-induced emission materials and subdiffraction patterning of graphene for devices. Next, to achieve a breakthrough in computational power, the applications of these super-resolution techniques for fabricating optical neural networks are further outlined. We also highlight the integration of graphene-based optoelectronic synapses with optical neural networks, enabling nonvolatile information processing and paving the way for fully integrated optical neuromorphic architectures that combine sensing, storage, and processing with unprecedented energy efficiency.

Neuromorphic computing systems convert multimodal signals to electrical responses for artificial intelligence recognition. Energy is consumed during both the response enhancement and depression, making the systems suffer from high energy consumption. This study presents a neuromorphic computing pathway based on supercapacitors. MXene Ti₃C₂Tx supercapacitors are fabricated and convert current stimuli to voltage responses. The response enhancement and depression are tunable through adjusting charging and discharging current stimuli, thus exhibiting synaptic plasticity. Typical synaptic behaviors are demonstrated, including short‐term memory, long‐term memory, paired‐pulse facilitation, and learning experience. Next, the voltage responses are used to recognize Braille numbers represented by 3 × 4 arrays. A charging/discharging current pulse train representing each Braille array is applied to the supercapacitor. The voltage responses are collected and converted to 12‐pixel greyscale images. Once the images representing Braille numbers 0–9 are input into artificial neural networks and deep diffraction neural networks, 100% accuracy can be achieved for recognizing the ten numbers. Because energy is stored during response enhancement in the supercapacitor and released once the response declines, this research demonstrates the potential applications of energy storage devices in neuromorphic computing, providing an innovative way to develop energy‐efficient brain‐like computing systems.