Room‐Temperature‐Processable Highly Reliable Resistive Switching Memory with Reconfigurability for Neuromorphic Computing and Ultrasonic Tissue Classification

Author:

Kim Dohyung12,Bang Hyeonsu3,Baac Hyoung Won4,Lee Jongmin12,Truong Phuoc Loc5,Jeong Bum Ho12,Appadurai Tamilselvan12,Park Kyu Kwan4,Heo Donghyeok6,Nam Vu Binh5,Yoo Hocheon7,Kim Kyeounghak8,Lee Daeho5,Ko Jong Hwan9,Park Hui Joon1210ORCID

Affiliation:

1. Department of Organic and Nano Engineering Hanyang University Seoul 04763 Korea

2. Human‐Tech Convergence Program Hanyang University Seoul 04763 Korea

3. Department of Artificial Intelligence Sungkyunkwan University Suwon 16419 Korea

4. Department of Electrical and Computer Engineering Sungkyunkwan University Suwon 16419 Korea

5. Department of Mechanical Engineering Gachon University Seongnam 13120 Korea

6. Department of Superintelligence Engineering Sungkyunkwan University Suwon 16419 Korea

7. Department of Electronic Engineering Gachon University Seongnam 13120 Korea

8. Department of Chemical Engineering Hanyang University Seoul 04763 Korea

9. College of Information and Communication Engineering Sungkyunkwan University Suwon 16419 Korea

10. Hanyang Institute of Smart Semiconductor Seoul 04763 Korea

Abstract

AbstractReversible metal‐filamentary mechanism has been widely investigated to design an analog resistive switching memory (RSM) for neuromorphic hardware‐implementation. However, uncontrollable filament‐formation, inducing its reliability issues, has been a fundamental challenge. Here, an analog RSM with 3D ion transport channels that can provide unprecedentedly high reliability and robustness is demonstrated. This architecture is realized by a laser‐assisted photo‐thermochemical process, compatible with the back‐end‐of‐line process and even applicable to a flexible format. These superior characteristics also lead to the proposal of a practical adaptive learning rule for hardware neural networks that can significantly simplify the voltage pulse application methodology even with high computing accuracy. A neural network, which can perform the biological tissue classification task using the ultrasound signals, is designed, and the simulation results confirm that this practical adaptive learning rule is efficient enough to classify these weak and complicated signals with high accuracy (97%). Furthermore, the proposed RSM can work as a diffusive‐memristor at the opposite voltage polarity, exhibiting extremely stable threshold switching characteristics. In this mode, several crucial operations in biological nervous systems, such as Ca2+ dynamics and nonlinear integrate‐and‐fire functions of neurons, are successfully emulated. This reconfigurability is also exceedingly beneficial for decreasing the complexity of systems—requiring both drift‐ and diffusive‐memristors.

Funder

National Research Foundation of Korea

Publisher

Wiley

Subject

Electrochemistry,Condensed Matter Physics,Biomaterials,Electronic, Optical and Magnetic Materials

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