Affiliation:
1. Division of Electronics and Electrical Engineering, Dongguk University, Seoul 04620, Republic of Korea
Abstract
In this study, we present the resistive switching characteristics and the emulation of a biological synapse using the ITO/IGZO/TaN device. The device demonstrates efficient energy consumption, featuring low current resistive switching with minimal set and reset voltages. Furthermore, we establish that the device exhibits typical bipolar resistive switching with the coexistence of non-volatile and volatile memory properties by controlling the compliance during resistive switching phenomena. Utilizing the IGZO-based RRAM device with an appropriate pulse scheme, we emulate a biological synapse based on its electrical properties. Our assessments include potentiation and depression, a pattern recognition system based on neural networks, paired-pulse facilitation, excitatory post-synaptic current, and spike-amplitude dependent plasticity. These assessments confirm the device’s effective emulation of a biological synapse, incorporating both volatile and non-volatile functions. Furthermore, through spike-rate dependent plasticity and spike-timing dependent plasticity of the Hebbian learning rules, high-order synapse imitation was done.
Funder
National R&D Program through the National Research Foundation of Korea
Subject
General Materials Science
Reference64 articles.
1. Milo, V., Malavena, G., Compagnoni, C.M., and Ielmini, D. (2020). Memristive and CMOS Devices for Neuromorphic Computing. Materials, 13.
2. Bridging Biological and Artificial Neural Networks with Emerging Neuromorphic Devices: Fundamentals, Progress, and Challenges;Tang;Adv. Mater.,2019
3. Reservoir Computing with Charge-Trap Memory Based on a MoS2 Channel for Neuromorphic Engineering;Farronato;Adv. Mater.,2023
4. Memristive Synapses and Neurons for Bioinspired Computing;Yang;Adv. Electron. Mater.,2019
5. Memristive Crossbar Arrays for Brain-Inspired Computing;Xia;Nat. Mater.,2019