# IGZO‐Based First Spike Timing Tactile Encoders and Coupling‐Enhanced Transistor Synapses for Efficient Spiking Neural Networks

## TL;DR

This paper introduces a compact neuromorphic system using IGZO-based tactile encoders and light-enhanced synapses to enable efficient spiking neural networks for tasks like autonomous vehicle detection and robotic navigation.

## Contribution

A novel IGZO-based hardware framework integrating tactile encoding and light-electric coupling synapses for fast and efficient spiking neural networks.

## Key findings

- PDTFT tactile encoders achieve millisecond-scale first-spike timing encoding.
- GaOx/IGZO synapses enable strong plasticity and overcome long-term memory limitations.
- The system achieves 98.4% accuracy in vehicle status detection and 98.2% in robotic navigation with 90.9% faster training.

## Abstract

Spike encoding is the fundamental prerequisite for the hardware implementation of event‐driven spiking neural networks (SNNs). However, compact device‐level realization of first‐spike‐timing (FST) encoding remains challenging, while high‐performance synaptic devices are urgently needed for efficient network training. Here, a light‐accelerated SNN hardware framework is proposed that integrates sensing, temporal encoding, and synaptic learning. A PDMS/MWCNTs film with IGZO dual‐TFTs (PDTFT) enables precise subthreshold modulation to restore the neuron “resting state,” achieving millisecond‐scale FST tactile encoding. Meanwhile, a GaOx/IGZO heterojunction introduced as a light‐electric coupling synapse (LECTS), where light supplements carriers and electrical bias modulates the barrier, overcoming the intrinsic lack of long‐term memory in IGZO and enabling stronger plasticity beyond single stimuli. Combining PDTFT and LECTS, autonomous‐vehicle status detection (98.4% accuracy) are demonstrated and smart robotic navigation (98.2% accuracy) with a 90.9% reduction in training time under supervised SNN learning. These results demonstrate a compact and highly‐efficient strategy for neuromorphic intelligence systems.

Here, a bioinspired light‐accelerated neuromorphic system that seamlessly links tactile sensing, first‐spike‐timing (FST) encoding, and light–electric synaptic learning. Pressure stimuli trigger FST spikes in dual‐gate PDTFTs, while GaOx/IGZO hetero‐synapses exhibit enhanced memory under optical–electrical co‐activation, enabling spiking neural networks to learn faster—with a remarkable 90.9% reduction in training time.