@misc{JiaPechmannMarkusetal., author = {Jia, Ruolan and Pechmann, Stefan and Markus, Fritscher and Wenger, Christian and Zhang, Lei and Hagelauer, Amelie}, title = {Soft-Error Analysis of RRAM 1T1R Compute-In-Memory Core for Artificial Neural Networks}, series = {2024 39th Conference on Design of Circuits and Integrated Systems (DCIS)}, journal = {2024 39th Conference on Design of Circuits and Integrated Systems (DCIS)}, publisher = {IEEE}, doi = {10.1109/DCIS62603.2024.10769203}, pages = {1 -- 5}, abstract = {This work analyses SEU-induced soft-errors in analog compute-in-memory cores using resistive random-access memory (RRAM) for artificial neural networks, where their bitcells utilize one-transistor-one-RRAM (1T1R) structure. This is modeled by combining the Stanford-PKU RRAM Model and the model of the radiation-induced photocurrent in access transistors. As results, this work derives the maximal RRAM crossbar size without occurring any logic flip and indicates the requirements for RRAM technology to achieve a SEU-resilient 1T1R compute-in memory cores.}, language = {en} }