A Capacitor-Based Synaptic Device with IGZO Access Transistors for Neuromorphic Computing

Abstract

IGZO TFT has extremely low leakage current characteristics compared to Si-CMOS, so it can be applied not only to displays but also to various research fields. One of the important applications using IGZO TFT is analog-based synaptic device for the efficient implementation for deep learning. The application of IGZO TFT to a synaptic device was more widely studied after a synaptic device based on Si-CMOS and capacitor was devised in 2017(1). Since insufficient retention time was a remaining problem in Si-CMOS and capacitor-based storage synaptic devices, the combination of IGZO TFT with low leakage current and capacitor was a very attractive solution. However, because the viable p-type metal oxide TFT does not exist, linearity and symmetry of weight update, a major requirement of on-chip trainable synaptic devices, has been difficult to achieve. In our research, for the first time, we obtained linear and symmetric weight update characteristics by combining an IGZO TFT and a capacitor.In addition, we verified the leakage characteristics of IGZO TFT and the retention characteristics of synaptic devices, which are the most important characteristics. Through the single TFT measurement result, we were able to obtain a leakage current per channel width 1µm of 10-17A, which is much smaller than the Si transistor. Lower leakage current will lead to better retention characteristics than conventional Si-CMOS-based devices, which will result in higher accuracy in deep neural network training. Based on the published results, the IGZO TFT synapse is projected to be able to achieve the retention time of ~4×109s using only 10fF capcacitor.Another important factor that affects deep neural network training is cell-to-cell and device-to-device variation. As opposed to capacitive synaptic devices, resistive synaptic devices such as Resistive RAM and Phase Change Memory suffer from large device variation. We also verified the device variation characteristics, which are advantages of charge storage devices based on transistors and capacitors. The variation characteristics of the device were evaluated through the ΔG+3σ/ΔG-3σ(%) formula and an excellent result of ~200% was obtained.In our research, we verified the main characteristics for on-chip training of synaptic devices based on IGZO TFT and capacitor: linearity and symmetry of weight update, retention characteristics and variation characteristics. The remarkable characteristics of synaptic devices based on IGZO TFT and capacitor will be a good candidate for on-chip training.1. S. Kim, T. Gokmen, H. M. Lee, and W. E. Haensch, Midwest Symp. Circuits Syst., 2017-Augus, 422–425 (2017).AcknowledgementsThis research was supported by National R&D Program through the National Research Foundation of Korea(NRF) funded by Ministry of Science and ICT(NRF-2020M3F3A2A01081240)