In-sensor optoelectronic computing using electrostatically doped silicon

Abstract

Complementary metal-oxide-semiconductor (CMOS) image sensors allow machines to interact with the visual world. In these sensors, image capture in front-end silicon photodiode arrays is separated from back-end image processing. To reduce the energy cost associated with transferring data between the sensing and computing units, in-sensor computing approaches are being developed where images are processed within the photodiode arrays. However, such methods require electrostatically doped photodiodes where photocurrents can be electrically modulated or programmed, and this is challenging in current CMOS image sensors that use chemically doped silicon photodiodes. Here we report in-sensor computing using electrostatically doped silicon photodiodes. We fabricate thousands of dual-gate silicon p-i-n photodiodes, which can be integrated into CMOS image sensors, at the wafer scale. With a 3 x 3 network of the electrostatically doped photodiodes, we demonstrate in-sensor image processing using seven different convolutional filters electrically programmed into the photodiode network. A network of dual-gate silicon p-i-n photodiodes, which are compatible with complementary metal-oxide-semiconductor fabrication processes, can perform in-sensor image processing by being electrically programmed into convolutional filters.