Using other-device-parameter values to compute the compliance-current (CC) value.
Published in AIP Advances on July 13, 2021. Read the full paper (open access).
Traditional physical-based models have generally been used to model the resistive-switching behavior of resistive-switching memory (RSM). Recently, vacancy-based conduction-filament (CF) growth models have been used to model device characteristics of a wide range of RSM devices. However, few have focused on learning the other-device-parameter values (e.g., low-resistance state, high-resistance state, set voltage, and reset voltage) to compute the compliance-current (CC) value that controls the size of CF, which can influence the behavior of RSM devices. Additionally, traditional CF growth models are typically physical-based models, which can show accuracy limitations. Machine learning holds the promise of modeling vacancy-based CF growth by learning other-device-parameter values to compute the CC value with excellent accuracy via examples, bypassing the need to solve traditional physical-based equations. Here, we sidestep the accuracy issues by directly learning the relationship between other-device-parameter values to compute the CC values via a data-driven approach with high accuracy for test devices and various device types using machine learning. We perform the first modeling with machine-learned device parameters on aluminum-nitride-based RSM devices and are able to compute the CC values for nitrogen-vacancy-based CF growth using only a few RSM device parameters. This model may now allow the computation of accurate RSM device parameters for realistic device modeling.
Karthekeyan Periasamy, Qishen Wang, Yi Fu, Shao-Xiang Go, Yu Jiang, Natasa Bajalovic, Jer-Chyi Wang, and Desmond. K. Loke , “Modeling electrical conduction in resistive-switching memory through machine learning”, AIP Advances 11, 075315 (2021) https://doi.org/10.1063/5.0052909
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