SC2024Top-tier venue
Accelerated Atomistic Kinetic Monte Carlo Simulations of Resistive Memory Arrays
Manasa Kaniselvan, Alexander Maeder, Marko Mladenovic, Mathieu Luisier, Alexandros Nikolaos Ziogas
Abstract
Simulating emerging resistive switching memory devices, such as memristors, requires modeling frameworks that can treat the motion of point defects across nanoscale domains. Field-driven Kinetic Monte Carlo (d-KMC) methods that simulate the discrete structural evolution of atomic coordinates in the presence of external potential and heat fields can be used for this purpose. While physically similar to conventional KMC methods, field-driven approaches present different computational motifs and introduce global communication. Here, we develop the first scalable d-KMC code for resistive memory arrays at atomistic resolution. We accelerate this latency-sensitive simulation on the GPU partition of the LUMI Supercomputer, exploiting the high-speed interconnects between GPUs on the same node. Applied to the technologically relevant HfOx material stack, our code enables the first atomistic simulation of arrays of resistive switching memory cells with more than 1 million atoms, matching the dimensions of fabricated structures.
Ask about this paper
Ask your agent about it.
Lune has read the top-tier papers around this one, so every answer names the papers it rests on.
Your agent calls
Lunesearch_papers
Free to start. No credit card required.
Terminal
Install the CLIlune papers get 1dcde8f2-f2af-428b-b1f3-2caee8fdff17Related papers
- MISA-AKMC : Achieve Kinetic Monte Carlo Simulation of 20 Quadrillion Atoms on GPU ClustersShunde Li, Zhijie Pan, Ningming Nie, Jue Wang et al.SC 2025 · 1 citation
- Extending the limit of molecular dynamics with ab initio accuracy to 10 billion atomsZhuoqiang Guo, Denghui Lu, Yujin Yan, Siyu Hu et al.PPoPP 2022 · 50 citations
- TensorKMC: kinetic Monte Carlo simulation of 50 trillion atoms driven by deep learning on a new generation of Sunway supercomputerHonghui Shang, Xin Chen, Xingyu Gao, Rongfen Lin et al.SC 2021 · 16 citations
- A massively parallel and scalable multi-CPU material point methodXinlei Wang, Yuxing Qiu, Stuart R. Slattery, Yu Fang et al.SIGGRAPH 2020 · 82 citations
- Towards Exascale Simulations of Nanoelectronic Devices in the GW ApproximationLeonard Deuschle, Alexander Maeder, Vincent Maillou, Nicolas Vetsch et al.SC 2024 · 3 citations
