Zinc Tin Oxide Synaptic Device for Neuromorphic Engineering

IEEE Access

卷:8

Seongjae Cho¹  Muhammad Imran²  Fayyaz Hussain³  Sungjun Kim⁴  Kyung Kyu Min⁵  Byung-Gook Park⁵  Tae-Hyeon Kim⁵  Yoon Kim⁶  Boram Kim⁶  Ji-Ho Ryu⁷  Chandreswar Mahata⁷  Muhammad Ismail⁷  Byung-Do Yang⁷  Teresa Oh⁸ 

[1] Department of Electronics Engineering, Gachon University, Seongnam, South Korea;

[2] Department of Physics, Government College University Faisalabad, Faisalabad, Pakistan;

[3] Department of Physics, Materials Research Simulation Laboratory (MSRL), Bahauddin Zakariya University, Multan, Pakistan;

[4] Division of Electronics and Electrical Engineering, Dongguk University, Seoul, South Korea;

[5] Inter-University Semiconductor Research Center (ISRC), Seoul National University, Seoul, South Korea;

[6] School of Electrical and Computer Engineering, University of Seoul, Seoul, South Korea;

[7] School of Electronics Engineering, Chungbuk National University, Cheongju, South Korea;

[8] School of Semiconductor Engineering, Cheongju University, Cheongju, South Korea;

关键词: Neuromorphic;    synaptic device;    zinc tin oxide;    density function theory;    neural network;   

DOI  :  10.1109/ACCESS.2020.3005303

来源: DOAJ

【 摘 要 】

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.

【 授权许可】

Unknown