4.8 Article

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

Journal

ADVANCED FUNCTIONAL MATERIALS
Volume 33, Issue 14, Pages -

Publisher

WILEY-V C H VERLAG GMBH
DOI: 10.1002/adfm.202213064

Keywords

3D ion transport channels; flexible; hardware learning rules; lasers; neuromorphic; reconfigurable; resistive switching memory; synapses

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An analog resistive switching memory with 3D ion transport channels is demonstrated using a laser-assisted photo-thermochemical process, enabling high reliability and robustness. This architecture also proposes a practical adaptive learning rule for hardware neural networks that simplifies voltage pulse application while maintaining high computing accuracy. Additionally, the memory can function as a diffusive-memristor, emulating crucial operations in biological nervous systems.
Reversible 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.

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