(Invited) IGZO-Based Neuromorphic Devices and Their Application in Spiking Neural Networks
Authors: Park Junhyeong , Yun Yumin , Bae Sunyeol , Lee Soo-Yeon 
Published: December 2024

Neuromorphic computing, which mimics the mechanism of the human brain, has attracted significant attention as a next-generation computing technology beyond von Neumann architecture owing to its ability to maximize the efficiency of information processing by connecting numerous synapses and neurons in parallel. Research areas ranging from hardware to networks are actively studied to realize neuromorphic computing. In hardware, research is focused on developing devices that can mimic the operations of synapses and neurons based on various materials, including silicon, two-dimensional material, and metal oxide. In particular, indium-gallium-zinc-oxide (IGZO) is one of the promising materials due to its low leakage current, moderate mobility, and CMOS compatibility. Furthermore, an IGZO-based neuromorphic system provides the advantage of low-temperature processing, enabling the implementation of highly integrated systems through monolithic 3D integration and fabrication on various platforms, such as glass and stretchable substrates. Regarding networks, research on spiking neural networks (SNNs) that mirror the biological mechanism of the human brain is primarily reported. SNN processes the information based on event-based spike propagation by using time-based encoding and integrate-and-fire (I&F) behavior, surpassing the energy efficiency of conventional deep neural networks. Consequently, it is expected that highly efficient information processing in various applications, such as image classification and voice recognition, can be achieved by adopting IGZO neuromorphic hardware and SNN.
In this study, we introduced our recent research on the charge trap-based IGZO synaptic transistor and integrate-and-fire (I&F) IGZO neuron circuit, along with SNN simulations for the IGZO neuromorphic system. The charge trap-based IGZO synaptic transistor is a suitable candidate for the artificial synapse due to its high weight linearity, wide dynamic range, and stable read/write operations through a 3-terminal structure. However, the synaptic transistor suffers from the inefficiency of charge trapping/de-trapping owing to the n-type IGZO channel that limits the utilization of holes during the charge transport between the charge trap layer (CTL) and the channel layer. To address this issue, we introduced IGZO as a CTL and optimized the IGZO deposition process to make it degenerate, allowing the electrons in the CTL to exist as free electrons for facilitating electron trapping/de-trapping. Using the synaptic transistor adopting the IGZO CTL, we successfully characterized superior long-term potentiation/depression, which are essential properties for a synapse. For an artificial neuron, a circuit capable of emulating I&F behavior is required. We proposed an I&F neuron circuit composed solely of n-type IGZO TFTs with a membrane capacitor and an inverter chain. Using these IGZO synaptic transistors and neuron circuits, we conducted SNN simulations for the IGZO neuromorphic system and evaluated the performance on three datasets: MNIST, fashion-MNIST, and Cifar-10. Additionally, we suggested a method to optimize the SNN system by adjusting the membrane capacitor of neurons to minimize the accuracy drop caused by information loss during the offline learning of SNN.

Figure 1