CMAS Lab - Statistical Analysis of Temperature Variability on the

Journal article

Sonal Shreya, Surila Guglani, Brajesh Kumar Kaushik, Sourajeet Roy
IEEE International Symposium on Quality Electronic Design, 2020

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APA Click to copy
Shreya, S., Guglani, S., Kaushik, B. K., & Roy, S. (2020). Statistical Analysis of Temperature Variability on the Write Efficiency of Spin-Orbit Torque MRAM using Polynomial Chaos Metamodels. IEEE International Symposium on Quality Electronic Design.

Chicago/Turabian Click to copy
Shreya, Sonal, Surila Guglani, Brajesh Kumar Kaushik, and Sourajeet Roy. “Statistical Analysis of Temperature Variability on the Write Efficiency of Spin-Orbit Torque MRAM Using Polynomial Chaos Metamodels.” IEEE International Symposium on Quality Electronic Design (2020).

MLA Click to copy
Shreya, Sonal, et al. “Statistical Analysis of Temperature Variability on the Write Efficiency of Spin-Orbit Torque MRAM Using Polynomial Chaos Metamodels.” IEEE International Symposium on Quality Electronic Design, 2020.

BibTeX Click to copy

@article{sonal2020a,
  title = {Statistical Analysis of Temperature Variability on the Write Efficiency of Spin-Orbit Torque MRAM using Polynomial Chaos Metamodels},
  year = {2020},
  journal = {IEEE International Symposium on Quality Electronic Design},
  author = {Shreya, Sonal and Guglani, Surila and Kaushik, Brajesh Kumar and Roy, Sourajeet}
}

Abstract

Analyzing the effects of temperature variability on the operation of spintronic devices is of great importance for memory applications. This is because temperature variations often induce variations in the switching metrics such as the critical current density and write energy. Traditionally, predictive analysis of the effect of temperature variations was done using a Monte Carlo framework. In this paper, a far more numerically efficient surrogate modeling approach based on the Polynomial Chaos (PC) technique is presented for thermal analysis of spin-orbit torque (SOT) based magnetic random access memory (MRAM). Importantly, for a wide range of temperature variation from -50°C to 120°C, it has been demonstrated that the PC technique is able to predict the statistical variations of current density (JSOT) and write energy (Ewrite) with more than 99.9% accuracy while offering between one to three orders of magnitude in speedup over the conventional Monte Carlo framework.