Synaptic Transistor Based on In‐Ga‐Zn‐O Channel and Trap Layers with Highly Linear Conductance Modulation for Neuromorphic Computing

Brain-inspired neuromorphic computing has drawn significant attraction as a promising technology beyond von Neumann architecture by using the parallel structure of synapses and neurons. Various artificial synapse configurations materials have been proposed to emulate synaptic behaviors for human brain functions such memorizing, learning, visual processing. Especially, memory type indium-gallium-zinc-oxide (IGZO) transistor adopting charge trapping layer (CTL) advantages high stability low leakage current IGZO channel. However, CTL material should be carefully selected optimized overcome de-trapping efficiency, resulting from difficulty in inducing holes In this paper, is adopted found out that making it degenerated crucial improving efficiency. The degenerate CTL, where electrons remain free electrons, induces Fowler-Nordheim tunneling increasing electric field across layer. As result, represents linearity potentiation (αp: −0.03) depression (αd: −0.47) with 64 conductance levels, which enables spiking neural network simulation achieve accuracy 98.08%. These experimental results indicate can one candidates applications.