IGZO-Based Synaptic Transistors and Their Neuromorphic Computing: From Device Mechanisms to Digital Recognition

In recent years, neuromorphic synaptic transistors have emerged as core components for building efficient neural network computing systems due to their unique ability to emulate biological synaptic plasticity. This article presents an oxide semiconductor-based neuromorphic synaptic transistor. The gate dielectric layer is fabricated using a simple solution spin-coating method, while an indium gallium zinc oxide (IGZO) channel is deposited via sputtering. Experimentally, the device demonstrates excellent optical responsivity and significant accumulation effects in response to optical signals. These photonic properties enable it to achieve single-pulse-induced excitatory postsynaptic currents (EPSCs), paired-pulse facilitation (PPF), learning-forgetting memory behaviors, and to emulate visual neural regulation under bright light conditions. Furthermore, image processing capabilities, including frequency-domain filtering, cutoff frequency testing, and high-pass filtering, have been demonstrated. A neuromorphic computing platform was constructed and trained using a custom-built dataset. Unlike traditional pattern recognition, which relies on manually annotated datasets and passive learning, our approach achieves near-perfect (>99%) testing accuracy after a defined number of training cycles. This work provides new insights for neuromorphic computing and offers a promising pathway for co-optimizing device design and system architecture in developing low-power, high-parallelism neuromorphic hardware.