Machine Learning Approach for Prediction of Point Defect Effect in FinFET
| Autor: | Meyya Meyyappan, Sun Jin Kim, Jungsik Kim, Jin-Woo Han |
|---|---|
| Rok vydání: | 2021 |
| Předmět: |
business.industry
Computer science medicine.medical_treatment Work (physics) Cognitive neuroscience of visual object recognition Process (computing) Hardware_PERFORMANCEANDRELIABILITY Machine learning computer.software_genre Electronic Optical and Magnetic Materials Data modeling Ion implantation Logic gate Hardware_INTEGRATEDCIRCUITS medicine Point (geometry) Artificial intelligence Electrical and Electronic Engineering Safety Risk Reliability and Quality business computer Decision analysis cycle |
| Zdroj: | IEEE Transactions on Device and Materials Reliability. 21:252-257 |
| ISSN: | 1558-2574 1530-4388 |
| Popis: | As Fin Field Effect Transistor (FinFET) scales aggressively, even a single point defect becomes a source of performance variability. The point defect is inevitably introduced not only by process damage such as epitaxial growth and ion implantation but also by cosmic rays. Technology computer-aided design (TCAD) is able to simulate the characteristics of the device with the defect. In this work, a machine learning algorithm is tested if it can reproduce the TCAD results. The impact of point defect in bulk FinFET is used as test vehicle to validate the machine-learning algorithm. TCAD is used first to generate a massive number of current-voltage characteristics dataset. The TCAD dataset is then exclusively divided into groups for machine learning training, validation and test. The trained model provides high accuracy test results within 1 % error, showing the possibility to expedite failure analysis cycle via machine learning. |
| Databáze: | OpenAIRE |
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