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Kim D, Lee CB, Park KK, Bang H, Truong PL, Lee J, Jeong BH, Kim H, Won SM, Kim DH, Lee D, Ko JH, Baac HW, Kim K, Park HJ. Highly Reliable 3D Channel Memory and Its Application in a Neuromorphic Sensory System for Hand Gesture Recognition. ACS NANO 2023; 17:24826-24840. [PMID: 38060577 DOI: 10.1021/acsnano.3c05493] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/27/2023]
Abstract
Brain-inspired neuromorphic computing systems, based on a crossbar array of two-terminal multilevel resistive random-access memory (RRAM), have attracted attention as promising technologies for processing large amounts of unstructured data. However, the low reliability and inferior conductance tunability of RRAM, caused by uncontrollable metal filament formation in the uneven switching medium, result in lower accuracy compared to the software neural network (SW-NN). In this work, we present a highly reliable CoOx-based multilevel RRAM with an optimized crystal size and density in the switching medium, providing a three-dimensional (3D) grain boundary (GB) network. This design enhances the reliability of the RRAM by improving the cycle-to-cycle endurance and device-to-device stability of the I-V characteristics with minimal variation. Furthermore, the designed 3D GB-channel RRAM (3D GB-RRAM) exhibits excellent conductance tunability, demonstrating high symmetricity (624), low nonlinearity (βLTP/βLTD ∼ 0.20/0.39), and a large dynamic range (Gmax/Gmin ∼ 31.1). The cyclic stability of long-term potentiation and depression also exceeds 100 cycles (105 voltage pulses), and the relative standard deviation of Gmax/Gmin is only 2.9%. Leveraging these superior reliability and performance attributes, we propose a neuromorphic sensory system for finger motion tracking and hand gesture recognition as a potential elemental technology for the metaverse. This system consists of a stretchable double-layered photoacoustic strain sensor and a crossbar array neural network. We perform training and recognition tasks on ultrasonic patterns associated with finger motion and hand gestures, attaining a recognition accuracy of 97.9% and 97.4%, comparable to that of SW-NN (99.8% and 98.7%).
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Affiliation(s)
- Dohyung Kim
- Department of Organic and Nano Engineering & Human-Tech Convergence Program, Hanyang University, Seoul 04763, Korea
| | - Cheong Beom Lee
- Department of Chemical Engineering, Hanyang University, Seoul 04763, Korea
| | - Kyu Kwan Park
- Department of Electrical and Computer Engineering, Sungkyunkwan University, Suwon 16419, Korea
| | - Hyeonsu Bang
- Department of Electrical and Computer Engineering, Sungkyunkwan University, Suwon 16419, Korea
| | - Phuoc Loc Truong
- Department of Mechanical Engineering, Gachon University, Gyeonggi 13120, Korea
| | - Jongmin Lee
- Department of Organic and Nano Engineering & Human-Tech Convergence Program, Hanyang University, Seoul 04763, Korea
| | - Bum Ho Jeong
- Department of Organic and Nano Engineering & Human-Tech Convergence Program, Hanyang University, Seoul 04763, Korea
| | - Hakjun Kim
- Department of Organic and Nano Engineering & Human-Tech Convergence Program, Hanyang University, Seoul 04763, Korea
| | - Sang Min Won
- Department of Electrical and Computer Engineering, Sungkyunkwan University, Suwon 16419, Korea
| | - Do Hwan Kim
- Department of Chemical Engineering, Hanyang University, Seoul 04763, Korea
| | - Daeho Lee
- Department of Mechanical Engineering, Gachon University, Gyeonggi 13120, Korea
| | - Jong Hwan Ko
- College of Information and Communication Engineering, Sungkyunkwan University, Suwon 16419, Korea
| | - Hyoung Won Baac
- Department of Electrical and Computer Engineering, Sungkyunkwan University, Suwon 16419, Korea
| | - Kyeounghak Kim
- Department of Chemical Engineering, Hanyang University, Seoul 04763, Korea
| | - Hui Joon Park
- Department of Organic and Nano Engineering & Human-Tech Convergence Program, Hanyang University, Seoul 04763, Korea
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Garlapati MM, Vaidya M, Karati A, Mishra S, Bhattacharya R, Murty B. Influence of Al content on thermal stability of nanocrystalline AlxCoCrFeNi high entropy alloys at low and intermediate temperatures. ADV POWDER TECHNOL 2020. [DOI: 10.1016/j.apt.2020.02.032] [Citation(s) in RCA: 24] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/27/2022]
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Phase transformations in reduced-activation duplex alloy Fe52Mn30Cr18 under isothermal heat treatment. FUSION ENGINEERING AND DESIGN 2019. [DOI: 10.1016/j.fusengdes.2019.111249] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/18/2022]
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