Dr. Yong-Jyun Wang received his Ph.D. in Materials Science and is currently working as a Postdoctoral Researcher, focusing on ionic devices. His research expertise encompasses oxide thin films, semiconductor devices, and neuromorphic computing devices for artificial intelligence hardware applications.

Recently, memristive devices have attracted researchers’ attention due to their extraordinary performance and multifunctional characteristics. The capability in non-volatile data storage and low-power operation triggers various potential applications in modern technology. Here, we report a two-terminal memristive device composed of a TiOx (switching layer)/Al₂O₃ (barrier layer) superlattice-like (SLL) artificial heterostructure. In this configuration, the multi-interfaces serve as a dominating factor to modulate the electrical properties. First, these multi-interfaces allow the device to show analog resistive switching and successfully mimic real synaptic behaviors. Besides fundamental capabilities such as potentiation/depression, an artificial neural network (ANN) simulation based on the measured synaptic characteristics demonstrates a handwritten digit recognition accuracy exceeding 90%, revealing a huge potential for neuromorphic hardware. On the other hand, the intrinsic stochasticity of conductance originating from interface modulation provides an ideal physical source for physical unclonable function (PUF) applications. Evaluation against machine learning modeling attacks demonstrates that the prediction accuracies successfully converge to the ideal 50%, indicating high resistance to reverse engineering. More importantly, the proposed device is a two-terminal design and fabricated in room temperature, showing a high compatibility with the back-end-of-line (BEOL) circuits. Overall, our work provides a simple yet effective strategy to combine advanced neuromorphic computing and hardware security into a single memory cell.

1. J. Pei, L. Deng, S. Song, M. Zhao, Y. Zhang, S. Wu, L. Wang, Z. Zou, Z. Wu, X. He, F. Chen, N. Deng, S. Wu, Y. Wang, Y. Yang, Z. Ma, G. Li, W. Liang, Y. Zhu, R. Zhao, L. Shi, Nature 572, 106 (2019), DOI: 10.1038/s41586-019-1424-8
2. K. Roy, A. Jaiswal, P. Panda, Nature 575, 607 (2019), DOI: 10.1038/s41586-019-1677-2