Multifaceted classification and integration of time-varying complex signals using analog neuromorphic UV phototransistors

Human vision encompasses a sophisticated sensory and computational system capable of analyzing complex attributes such as color, intensity, duration, and nature (linear or non-linear) of light exposure, along with storing its termination details. Inspired by this, neuromorphic vision sensors have been developed to enhance real-time data processing and decision-making, surpassing conventional sensors. However, they fall short in accurate multifaceted classification, integration of complex and temporal patterns, and secure storage of outcomes, which are critical for precise monitoring, security, and forecasting of dynamic natural phenomena. Here, we present a generic approach to developing a neuromorphic optical sensor adept at multifaceted classification and integration of non-linear inputs. Leveraging the heterogeneity in UV response of two-dimensional electron gas-based thin-film transistors and robust persistent photoconductivity, our sensor precisely discriminates between 310, 365, and 395 nm wavelengths, mixed wavelengths, while tracking both the duration and termination of illumination. The sensor offers highly secure, in-sensor multibit data processing and storage with a high on/off ratio exceeding 10 7 . Furthermore, it adeptly handles real-time dynamic sensing, integration, and revelation of both linear and non-linear optical inputs as dictated by differential equations, including logistic maps, and is capable of monitoring complex dynamic phenomena such as those caused by water vortices. We present neuromorphic optical sensor for multifaceted classification. It discriminates 310, 365, and 395 nm wavelengths, tracks illumination duration and termination, real-time dynamic sensing, and monitors complex phenomena like water vortices.