ScholarWorks@Y-Scholar Hub

초록

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 x 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.,A groundbreaking method enabled the wide bandgap IGZO, widely used in commercial thin-film transistors, to extend absorption into the visible range via metallic sensitization islands, achieving a spectral response without additional absorption layer.Swiftly scalable metallic sensitization islands-mediated artificial synapses with enhanced weight modulation were demonstrated for retinomorphic hardware neural networks through a single-step, CMOS-compatible process.A record-superlinear relationship with a dynamic range of 64.01 and nonlinearity below 0.1, highlights feasibility of hardware neural network implementation.,