Zinc Tin Oxide Synaptic Device for Neuromorphic Engineering

• Published in: IEEE access Vol. 8; p. 1
• Main Authors: Ryu, Ji-Ho, Kim, Boram, Hussain, Fayyaz, Ismail, Muhammad, Mahata, Chandreswar, Oh, Teresa, Imran, Muhammad, Min, Kyung Kyu, Kim, Tae-Hyeon, Yang, Byung-Do, Cho, Seongjae, Park, Byung-Gook, Kim, Yoon, Kim, Sungjun
• Format: Journal Article
• Language: English
• Published: Piscataway IEEE 01-01-2020; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Charge density; CMOS; Computation; Data processing; Denial of service attacks; density function theory; Density of states; Electrodes; Energy consumption; Hardware; Insulators; Memristors; Metal oxides; MIS (semiconductors); neural network; Neural networks; Neuromorphic; Neuromorphic computing; Neuromorphics; Nickel; Pattern recognition; Polysilicon; Resistance; Silicon; Switches; Switching; Synapses; synaptic device; Tin oxides; Voltage control; Zinc; zinc tin oxide
• ISSN: 2169-3536; 2169-3536
• DOI: 10.1109/ACCESS.2020.3005303

Neuromorphic computing offers parallel data processing and low energy consumption and can be useful to replace conventional von Neumann computing. Memristors are two-terminal devices with varying conductance that can be used as synaptic arrays in hardware-based neuromorphic devices. In this research, we extensively investigate the analog symmetric multi-level switching characteristics of zinc tin oxide (ZTO)-based memristor devices for neuromorphic systems. A ZTO semiconductor layer is introduced between a complementary metal-oxide-semiconductor (CMOS) compatible Ni top electrode and a highly doped poly-Si bottom electrode. A variety of bio-realistic synaptic features are demonstrated, including long-term potentiation (LTP), long-term depression (LTD), and spike timing-dependent plasticity (STDP). The Ni/ZTO/Si device in which the adjustment of the number of states in conductance is realized by applying different pulse schemes is highly suitable for hardware-based neuromorphic applications. We evaluate the pattern recognition accuracy by implementing a system-level neural network simulation with ZTO-based memristor synapses. The density of states (DOS) and charge density plots reveal that oxygen vacancies in ZTO assist in generating resistive switching in the Ni/ZTO/Si device. The proposed ZTO-based memristor composed of metal-insulator-semiconductor (MIS) structure is expected to contribute to future neuromorphic applications through further studies.