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Modeling the effect of technology trends on the soft error rate of combinational logic

Shivakumar, P., Kistler, M., Keckler, S.W., Burger, D., Alvisi, L.·2003·Proceedings International Conference on Dependable Systems and Networks

Abstract

This paper examines the effect of technology scaling and microarchitectural trends on the rate of soft errors in CMOS memory and logic circuits. We describe and validate an end-to-end model that enables us to compute the soft error rates (SER) for existing and future microprocessor-style designs. The model captures the effects of two important masking phenomena, electrical masking and latching-window masking, which inhibit soft errors in combinational logic. We quantify the SER due to high-energy neutrons in SRAM cells, latches, and logic circuits for feature sizes from 600 nm to 50 nm and clock periods from 16 to 6 fan-out-of-4 inverter delays. Our model predicts that the SER per chip of logic circuits will increase nine orders of magnitude from 1992 to 2011 and at that point will be comparable to the SER per chip of unprotected memory elements. Our result emphasizes that computer system designers must address the risks of soft errors in logic circuits for future designs.

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Metadata

Authors
Shivakumar, P., Kistler, M., Keckler, S. W., Burger, D., & Alvisi, L.
Venue
Proceedings International Conference on Dependable Systems and Networks
Published
2003-06-25
Pages
389-398
OpenAlex
W2169213530
Cited by
1,449
References
7
FWCI
113.06
Type
conference paper
Language
en
Open access
closed

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CoChat. (2026). Citation check: Modeling the effect of technology trends on the soft error rate of combinational logic. https://app.cochat.ai/papers/10.1109/dsn.2002.1028924
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Shivakumar, P., Kistler, M., Keckler, S. W., Burger, D., & Alvisi, L. (2003). Modeling the effect of technology trends on the soft error rate of combinational logic. Proceedings International Conference on Dependable Systems and Networks. https://doi.org/10.1109/dsn.2002.1028924

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Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.