Physics-Informed Multi-Parameter Optimization of Oxide-Based Memristor Characteristics Using Genetic Algorithms
- 1University of Oklahoma , , , ,
Abstract
We present a multiobjective, physics-informed optimization framework for oxide-based memristive devices that jointly minimizes forming voltage (Vf) and maximizes memory contrast (HRS/LRS). The proposed approach integrates COMSOL Multiphysics with MATLAB through a genetic algorithm (GA), enabling self-consistent electrothermal simulations and systematic exploration of a high-dimensional design space encompassing geometrical, electrical, thermal, and operational parameters. By optimizing 13 key variables, the GA efficiently identifies optimal trade-offs between switching efficiency and resistance ratio. The optimization results demonstrate a substantial enhancement in device performance, with the high-resistance-state to low-resistance-state ratio (HRS/LRS) improved by up to 21× relative to the reference device, while alternative optimized configurations achieve a reduction in Vf down to 0.7× of the reference value. A parameter-importance analysis based on a random forest model highlights oxide thickness and effective electrical and thermal conductivities as influential parameters. Importantly, the optimization further reveals nontrivial multiparameter trade-offs, demonstrating that reductions in Vf and enhancements in HRS/LRS can be simultaneously achieved through coordinated tuning of geometrical, material, and pulse-related parameters beyond simple intuitive design rules. Overall, the MATLAB–COMSOL co-optimization strategy provides a scalable and generalizable computational design methodology for next-generation low-power, high-contrast RRAM devices, and offers quantitative insights into the coupled physical mechanisms underlying filament formation and rupture.
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BibTeX
@article{gooranshoorakchaly2026physicsinformed,
title = {{Physics-Informed Multi-Parameter Optimization of Oxide-Based Memristor Characteristics Using Genetic Algorithms}},
author = {Armin Gooran-Shoorakchaly and Sarah Sharif and Yaser Banad},
journal = {ACS Omega},
year = {2026},
month = oct,
doi = {10.1021/acsomega.6c01327},
url = {https://pubs.acs.org/acsodf/article/11/42/64416/5449402/Physics-Informed-Multi-Parameter-Optimization-of},
note = {CC BY-NC-ND 4.0},
}
RIS
TY - JOUR
TI - Physics-Informed Multi-Parameter Optimization of Oxide-Based Memristor Characteristics Using Genetic Algorithms
AU - Armin Gooran-Shoorakchaly
AU - Sarah Sharif
AU - Yaser Banad
PY - 2026
DA - 2026/10/12
JO - ACS Omega
DO - 10.1021/acsomega.6c01327
UR - https://pubs.acs.org/acsodf/article/11/42/64416/5449402/Physics-Informed-Multi-Parameter-Optimization-of
AB - We present a multiobjective, physics-informed optimization framework for oxide-based memristive devices that jointly minimizes forming voltage (Vf) and maximizes memory contrast (HRS/LRS). The proposed approach integrates COMSOL Multiphysics with MATLAB through a genetic algorithm (GA), enabling self-consistent electrothermal simulations and systematic exploration of a high-dimensional design space encompassing geometrical, electrical, thermal, and operational parameters. By optimizing 13 key variables, the GA efficiently identifies optimal trade-offs between switching efficiency and resistance ratio. The optimization results demonstrate a substantial enhancement in device performance, with the high-resistance-state to low-resistance-state ratio (HRS/LRS) improved by up to 21× relative to the reference device, while alternative optimized configurations achieve a reduction in Vf down to 0.7× of the reference value. A parameter-importance analysis based on a random forest model highlights oxide thickness and effective electrical and thermal conductivities as influential parameters. Importantly, the optimization further reveals nontrivial multiparameter trade-offs, demonstrating that reductions in Vf and enhancements in HRS/LRS can be simultaneously achieved through coordinated tuning of geometrical, material, and pulse-related parameters beyond simple intuitive design rules. Overall, the MATLAB–COMSOL co-optimization strategy provides a scalable and generalizable computational design methodology for next-generation low-power, high-contrast RRAM devices, and offers quantitative insights into the coupled physical mechanisms underlying filament formation and rupture.
SN - 2470-1343
ER -