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An SRAM SEU Cross Section Curve Physics Model
- Source :
- IEEE Transactions on Nuclear Science. 69:232-240
- Publication Year :
- 2022
- Publisher :
- Institute of Electrical and Electronics Engineers (IEEE), 2022.
-
Abstract
- Static random access memories (SRAMs) are prone to a single-event upset (SEU), also known as soft errors, due to transient noise caused by a single strike of radiation. Beam testing has been extensively used to measure the SEU cross section of SRAMs as a function of the linear energy transfer (LET) of charged particle radiation. The evolution of the cross section as a function of LET is called the cross section curve, which plays a vital role in upset rate analysis for hardness assurance. Various analytical models have been developed to describe SRAM SEU cross-section curves, and they have proven to be useful in reducing the cost of beam testing as well as revealing the physics behind test results. However, they involve arbitrary parameters, which make it challenging to predict cross-section curves without any beam results. Moreover, the current method of analyzing cross-section curves or the LET dependence of cross sections relies on a model different from that is used in the analysis of power-supply-voltage dependence, which is becoming increasingly important because of the demand for low-power operation. To overcome these problems, this paper proposes a unified equation that describes both LET and the power-supply-voltage dependence of SRAM SEU cross sections. It comprises only parameters that are physically clear and familiar to SEU researchers. As well as giving possible constraints, comparisons with data from the literature suggest it can be applied to SRAMs fabricated in bulk and silicon-on-insulator (SOI) processes across generations from the early 1000-nm-scale to the current 10-nm-scale technology nodes.
Details
- ISSN :
- 15581578 and 00189499
- Volume :
- 69
- Database :
- OpenAIRE
- Journal :
- IEEE Transactions on Nuclear Science
- Accession number :
- edsair.doi...........9971f9703e97e1b1a189344c496e1372
- Full Text :
- https://doi.org/10.1109/tns.2021.3129185