Neuromorphic Vision Computing

Neuromorphic computing, referred to as brain-inspired computing for big-data processing and accelerating artificial intelligence (AI) computation, has received a significant boost from the emergence of memristors and associated computing algorithms over the past decade. Recent advancements in memristive systems have enabled the integration of sensing and computing on a chip, known as in-sensor computing, leveraging the memory and dynamic processing capabilities associated with synaptic long-term and short-term plasticity. Among the senses, vision plays a pivotal role in information processing, enabling remote sensing for navigation, learning, and communication. While current neuromorphic systems utilizing advanced memristors have primarily focused on two-dimensional (2D) vision applications, akin to human visual perception, three-dimensional (3D) vision is also vital for machines to tackle more complex tasks by obtaining additional depth information. In this dissertation, we present a comprehensive approach to neuromorphic vision computing that encompasses both 2D and 3D information processing in conjunction with artificial vision dynamics. We demonstrate one III-V photodiode and one nonvolatile memristor (1P1R) array capable of visual sensing, memory, and computing functions. This enables in-sensor computing protocols such as in-situ visual classification and encoding, referred to as 2D neuromorphic vision computing. We also introduce a bio-inspired 3D sensing technique utilizing nonvolatile memristors, known as the resistive time-of-flight (RToF) principle, enabling unprecedented 3D neuromorphic vision computing. we lastly achieve dynamic bio-inspired vision by integrating conventional high-electron-mobility transistors (HEMTs) with emerging 2D ferroelectric materials that emulate synaptic plasticity, potentially enabling mixed 2D/3D neuromorphic vision. This multidimensional approach to neuromorphic vision computing paves the way for empowering advanced computer vision and augmented reality applications.