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

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.