Swiftly accessible retinomorphic hardware for in-sensor image preprocessing and recognition: IGZO-based neuro-inspired optical image sensor arrays with metallic sensitization island
Kyungmoon Kwak, Kyung-Ho Park, Jae Seong Han, Byung Ha Kang, Dong Hyun Choi, Kunho Moon, Seokmoo Hong, Gwan In Kim, Ju‐Hyun Lee, Hyun Jae Kim
Abstract
Abstract In-optical-sensor computing architectures based on neuro-inspired optical sensor arrays have become key milestones for in-sensor artificial intelligence (AI) technology, enabling intelligent vision sensing and extensive data processing. These architectures must demonstrate potential advantages in terms of mass production and complementary metal oxide semiconductor compatibility. Here, we introduce a visible-light-driven neuromorphic vision system that integrates front-end retinomorphic photosensors with a back-end artificial neural network (ANN), employing a single neuro-inspired indium-gallium-zinc-oxide phototransistor (NIP) featuring an aluminum sensitization layer (ASL). By methodically adjusting the ASL coverage on IGZO phototransistors, a fast-switching response-type and a synaptic response-type of IGZO phototransistors are successfully developed. Notably, the fabricated NIP shows a remarkable retina-like photoinduced synaptic plasticity under wavelengths up to 635 nm, with over 256-states, weight update nonlinearity below 0.1, and a dynamic range of 64.01. Owing to this technology, a 6 × 6 neuro-inspired optical image sensor array with the NIP can perform highly integrated sensing, memory, and preprocessing functions, including contrast enhancement, and handwritten digit image recognition. The demonstrated prototype highlights the potential for efficient hardware implementations in in-sensor AI technologies.