All-Optical Synapses Enabled by Photochromic Materials for High-Accuracy Optical Signal Recognition

Abstract

Developing artificial synapses capable of optical signal recognition is crucial for advancing neuromorphic computing. However, achieving high accuracy of synapse-based optical signal recognition, which avoids complex procedures of electrode fabrication and follows a contactless pathway, remains a significant challenge. In this study, we utilize a photochromic film of chemically synthesized WO₃ with a transmission modulation of 77% to construct all-optical artificial synapses. Unlike optoelectronic approaches, the synapses leverage light for stimulation and contactless response measurement. Typical synaptic behaviors, including paired-pulse facilitation, learning experience, short-term memory, and long-term memory, can be demonstrated through transmittance responses under UV beam stimulation. Furthermore, the WO₃ thin film can simulate the memory behavior of human skin’s UV detection when transitioning from outdoor to indoor environments and back to outdoor conditions. Next, a recurrent neural network processes the synaptic transmittance responses to recognize optical signals, achieving 100% accuracy for preset light exposure durations and powers, 95% accuracy for closely spaced durations with a 1 s difference, and 100% accuracy for power differences of 14.5 mW. Moreover, 26 English alphabet letters encoded to different optical pulse trains can be recognized using an all-optical artificial synapse integrated with a recurrent neural network, achieving 100% accuracy. This work highlights the potential of photochromic materials in enabling high-performance neuromorphic computing and provides a new pathway for integrating optical signal processing with artificial intelligence.