Predicting failure parameters of semiconductor devices subjected to stress conditions
Abstract
A method for predicting failure parameters of semiconductor devices can include receiving a set of data that includes (i) characteristics of a sample semiconductor device, and (ii) parameters characterizing a stress condition. The method further includes extracting a plurality of feature values from the set of data and inputting the plurality of feature values into a trained model executing on the one or more processors, wherein the trained model is configured according to an artificial intelligence (AI) algorithm based on a previous plurality of feature values, and wherein the trained model is operable to output a failure prediction based on the plurality of feature values. Further, the method includes generating, via the trained model, a predicted failure parameter of the sample semiconductor device due to the stress condition.
- Inventors:
- Issue Date:
- Research Org.:
- Argonne National Laboratory (ANL), Argonne, IL (United States)
- Sponsoring Org.:
- USDOE
- OSTI Identifier:
- 1986795
- Patent Number(s):
- 11493548
- Application Number:
- 17/383,776
- Assignee:
- UChicago Argonne, LLC (Chicago, IL)
- DOE Contract Number:
- AC02-06CH11357
- Resource Type:
- Patent
- Resource Relation:
- Patent File Date: 07/23/2021
- Country of Publication:
- United States
- Language:
- English
Citation Formats
Ahmed, Moinuddin, Hryn, John N., and Stankus, Christopher. Predicting failure parameters of semiconductor devices subjected to stress conditions. United States: N. p., 2022.
Web.
Ahmed, Moinuddin, Hryn, John N., & Stankus, Christopher. Predicting failure parameters of semiconductor devices subjected to stress conditions. United States.
Ahmed, Moinuddin, Hryn, John N., and Stankus, Christopher. Tue .
"Predicting failure parameters of semiconductor devices subjected to stress conditions". United States. https://www.osti.gov/servlets/purl/1986795.
@article{osti_1986795,
title = {Predicting failure parameters of semiconductor devices subjected to stress conditions},
author = {Ahmed, Moinuddin and Hryn, John N. and Stankus, Christopher},
abstractNote = {A method for predicting failure parameters of semiconductor devices can include receiving a set of data that includes (i) characteristics of a sample semiconductor device, and (ii) parameters characterizing a stress condition. The method further includes extracting a plurality of feature values from the set of data and inputting the plurality of feature values into a trained model executing on the one or more processors, wherein the trained model is configured according to an artificial intelligence (AI) algorithm based on a previous plurality of feature values, and wherein the trained model is operable to output a failure prediction based on the plurality of feature values. Further, the method includes generating, via the trained model, a predicted failure parameter of the sample semiconductor device due to the stress condition.},
doi = {},
journal = {},
number = ,
volume = ,
place = {United States},
year = {2022},
month = {11}
}
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