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

Automated summary

Brain-inspired neuromorphic computing, using the parallel structure of synapses and neurons, is a promising technology beyond von Neumann architecture. This paper explores the use of indium-gallium-zinc-oxide (IGZO) synaptic transistors with a charge trapping layer (CTL) and finds that degenerating the CTL is crucial for improving the de-trapping efficiency. The experimental results demonstrate high linearity of potentiation and depression, with 64 conductance levels, enabling accurate spiking neural network simulations.