MnO2/WO3 electrochromic synapses for optical neuromorphic computing with adaptation effect

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.