Variability Analysis of Multilevel Spin-Orbit Torque MRAMs using Machine Learning

Journal article

Anubha Sehgal, K. Das, Seema Dhull, Sourajeet Roy, Brajesh Kumar Kaushik
2023 IEEE 23rd International Conference on Nanotechnology (NANO), 2023

Semantic Scholar DOI

Cite

APA Click to copy
Sehgal, A., Das, K., Dhull, S., Roy, S., & Kaushik, B. K. (2023). Variability Analysis of Multilevel Spin-Orbit Torque MRAMs using Machine Learning. 2023 IEEE 23rd International Conference on Nanotechnology (NANO).

Chicago/Turabian Click to copy
Sehgal, Anubha, K. Das, Seema Dhull, Sourajeet Roy, and Brajesh Kumar Kaushik. “Variability Analysis of Multilevel Spin-Orbit Torque MRAMs Using Machine Learning.” 2023 IEEE 23rd International Conference on Nanotechnology (NANO) (2023).

MLA Click to copy
Sehgal, Anubha, et al. “Variability Analysis of Multilevel Spin-Orbit Torque MRAMs Using Machine Learning.” 2023 IEEE 23rd International Conference on Nanotechnology (NANO), 2023.

BibTeX Click to copy

@article{anubha2023a,
  title = {Variability Analysis of Multilevel Spin-Orbit Torque MRAMs using Machine Learning},
  year = {2023},
  journal = {2023 IEEE 23rd International Conference on Nanotechnology (NANO)},
  author = {Sehgal, Anubha and Das, K. and Dhull, Seema and Roy, Sourajeet and Kaushik, Brajesh Kumar}
}

Abstract

In this paper, an artificial neural network (ANN) based surrogate modeling is performed to estimate the variability in multilevel spin-orbit torque magnetic random-access memory (SOT-MRAM). ANN is utilized to predict the impact of variations in device parameters such as oxide thickness, free layer thickness, tunnel magneto-resistance (TMR), and temperature on the resistance and write energy (Ewrite). The results demonstrate that the ANN approach is suited for fast computation when compared with Monte-Carlo framework offering a thousand orders of speedup in magnitude with 99.5%, 98.98%, 98.59%, and 97.99% accuracy respectively for different resistance values (R00, R01, R10, R11).