Ultraviolet Light-Driven Artificial Neuromorphic Properties in Organic Transistors for Reservoir Computing and Encrypted Communication

Organic semiconductors exhibit significant potential for application in artificial neuromorphic computing because of their unique electrical and optoelectronic properties. In this study, we explore the potential of the nonfullerene Y6 (also known as BTP-4F) organic semiconductor material in optoelectronic neuromorphic computing, image recognition, reservoir computing (RC), and wireless encrypted communication. The organic field-effect transistor (OFET) constructed using the Y6 polymer exhibits n-type semiconductor behavior with a current on–off ( I_ON / I_OFF ) ratio of approximately 10² as well as excellent synaptic functionalities under ultraviolet (UV) light. The synaptic plasticity of OFET is demonstrated to be optically controllable using incident light of 220-nm wavelength. Notably, the transition from short-term memory (STM) to long-term memory (LTM) could be modulated by manipulating pulse time, pulse number, and pulse interval. Furthermore, image recognition on the Modified National Institute of Standards and Technology (MNIST) dataset using the fabricated device in conjunction with a convolutional neural network (CNN) is observed to yield an excellent accuracy of 93.4%. Moreover, we demonstrate the application of Y6 OFET in a 4-bit RC for digit classification. Finally, optically encrypted communication is also achieved based on the international Morse code. These findings demonstrate the potential of Y6-based OFETs in neuromorphic computing, RC, and encrypted communication, paving the way for innovations in brain-inspired computing and artificial intelligence (AI) hardware.