Security enhancement of artificial neural network using physically transient form of heterogeneous memristors with tunable resistive switching behaviors

Abstract

As a critical command center in organisms, the brain can execute multiple intelligent interactions through neural networks, including memory, learning and cognition. Recently, memristive-based neuromorphic devices have been widely developed as promising technologies to build artificial synapses and neurons for neural networks. However, multiple information interactions in artificial intelligence devices potentially pose threats to information security. Herein, a transient form of heterogeneous memristor with a stacked structure of Ag/MgO/SiN_x/W is proposed, in which both the reconfigurable resistive switching behavior and volatile threshold switching characteristics could be realized by adjusting the thickness of the SiN_x layer. The underlying resistive switching mechanism of the device was elucidated in terms of filamentary and interfacial effects. Representative neural functions, including short-term plasticity (STP), the transformation from STP to long-term plasticity, and integrate-and-fire neuron functions, have been successfully emulated in memristive devices. Moreover, the dissolution kinetics associated with underlying transient behaviors were explored, and the water-assisted transfer printing technique was exploited to build transient neuromorphic device arrays on the water-dissolvable poly(vinyl alcohol) substrate, which were able to formless disappear in deionized water after 10-s dissolution at room temperature. This transient form of memristive-based neuromorphic device provides an important step toward information security reinforcement for artificial neural network applications.