Ultrafast Nanoscale Gradient Junction Self-Powered Schottky Photodetectors for Vision-Like Object Classification

Abstract

Emulating human vision for pattern classification is essential in intelligent machines, including pilotless vehicles and humanoid robots. Traditionally, analog visual information is captured by photosensors, which are sequentially classified using a physically separated algorithm-based unit, such as a computer. Among such units, a self-powered photodetector with a quick information sensing feature can be utilized for an unprecedented pattern classification; however, designing an ultrafast photodetector while maintaining a high sensitivity is critical. Herein, an alternative photovoltaic-effect-based, highly sensitive, self-powered, broadband, ultrafast gradient junction nanoscale Schottky photodetector is introduced, that can sense input optical information without latency. Importantly, the proposed device can sense optical input within a ≈40 ns duration and demonstrate a 3-dB bandwidth wider than 0.5 MHz, providing a throughput of 10 million bits per second. Photoconductive atomic force microscopy and Kelvin probe force microscopy independently revealed the photodynamic characteristic at a nanometer (≈35 nm) scale. Further, an array that can classify nontrivial patterns even with noisy inputs is developed. A unique solution to achieve an ultrafast photo response in self-powered mode and classify the input patterns is provided. The proposed approach can be extended to several other artificial neural sensors, such as tactile, audio, and thermal sensors.