BEOL Integration of Reconfigurable Li-Based Memristors on Foundry CMOS Chips

Li-based memristors have been found to have both volatile and non-volatile resistive switching behavior, which can be reconfigured for computation tasks like neuromorphic computing, showing its potential in novel energy efficient computing architecture. However, the incompatibility of Li-based materials with current CMOS production facility makes it almost impossible to have foundry fabricate Li-based memristors. We hereby propose a BEOL method to integrate Li-based memristors with foundry CMOS chips using an academic fabrication facility. The proposed BEOL integration method can also be generalized to other CMOS process-incompatible devices.

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Publication Details

Published
2024-10-21
DOI
https://doi.org/10.1109/nmdc58214.2024.10894469
Primary Topic
Advanced Memory and Neural Computing
Type
article
Field-Weighted Citation Impact
0.00

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article

BEOL Integration of Reconfigurable Li-Based Memristors on Foundry CMOS Chips

Rabiul Islam, Yu Shi, Guo‐Xing Miao, Manoj Sachdev
Advanced Memory and Neural Computing
article

BEOL Integration of Reconfigurable Li-Based Memristors on Foundry CMOS Chips

Rabiul Islam, Yu Shi, Guo‐Xing Miao, Manoj Sachdev
article en

Abstract

Li-based memristors have been found to have both volatile and non-volatile resistive switching behavior, which can be reconfigured for computation tasks like neuromorphic computing, showing its potential in novel energy efficient computing architecture. However, the incompatibility of Li-based materials with current CMOS production facility makes it almost impossible to have foundry fabricate Li-based memristors. We hereby propose a BEOL method to integrate Li-based memristors with foundry CMOS chips using an academic fabrication facility. The proposed BEOL integration method can also be generalized to other CMOS process-incompatible devices.

University of Waterloo (CA)
Canadian Network for Research and Innovation in Machining Technology, Natural Sciences and Engineering Research Council of Canada
Affordable and clean energy
Openalex Percentile: Top 74%
Advanced Memory and Neural Computing
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