A Review of Machine Learning Techniques in Analog Integrated Circuit Design Automation
Analog integrated circuit design is widely considered a time-consuming task due to the acute dependence of analog performance on the transistors’ and passives’ dimensions. An important research effort has been conducted in the past decade to reduce the front-end design cycles of analog circuits by m...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-01-01
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| Series: | Electronics |
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| Online Access: | https://www.mdpi.com/2079-9292/11/3/435 |
| _version_ | 1797488309526593536 |
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| author | Rayan Mina Chadi Jabbour George E. Sakr |
| author_facet | Rayan Mina Chadi Jabbour George E. Sakr |
| author_sort | Rayan Mina |
| collection | DOAJ |
| description | Analog integrated circuit design is widely considered a time-consuming task due to the acute dependence of analog performance on the transistors’ and passives’ dimensions. An important research effort has been conducted in the past decade to reduce the front-end design cycles of analog circuits by means of various automation approaches. On the other hand, the significant progress in high-performance computing hardware has made machine learning an attractive and accessible solution for everyone. The objectives of this paper were: (1) to provide a comprehensive overview of the existing state-of-the-art machine learning techniques used in analog circuit sizing and analyze their effectiveness in achieving the desired goals; (2) to point out the remaining open challenges, as well as the most relevant research directions to be explored. Finally, the different analog circuits on which machine learning techniques were applied are also presented and their results discussed from a circuit designer perspective. |
| first_indexed | 2024-03-10T00:00:17Z |
| format | Article |
| id | doaj.art-54d802f1cbbd462e9d853ab073f7bab7 |
| institution | Directory Open Access Journal |
| issn | 2079-9292 |
| language | English |
| last_indexed | 2024-03-10T00:00:17Z |
| publishDate | 2022-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Electronics |
| spelling | doaj.art-54d802f1cbbd462e9d853ab073f7bab72023-11-23T16:16:54ZengMDPI AGElectronics2079-92922022-01-0111343510.3390/electronics11030435A Review of Machine Learning Techniques in Analog Integrated Circuit Design AutomationRayan Mina0Chadi Jabbour1George E. Sakr2Department of Electrical and Mechanical Engineering, Saint-Joseph University of Beirut, Beirut 1004 2020, LebanonCOMELEC, Institut Mines-Télécom Paris, 91120 Palaiseau, FranceVirgil Systems, Toronto, ON M5G 1E2, CanadaAnalog integrated circuit design is widely considered a time-consuming task due to the acute dependence of analog performance on the transistors’ and passives’ dimensions. An important research effort has been conducted in the past decade to reduce the front-end design cycles of analog circuits by means of various automation approaches. On the other hand, the significant progress in high-performance computing hardware has made machine learning an attractive and accessible solution for everyone. The objectives of this paper were: (1) to provide a comprehensive overview of the existing state-of-the-art machine learning techniques used in analog circuit sizing and analyze their effectiveness in achieving the desired goals; (2) to point out the remaining open challenges, as well as the most relevant research directions to be explored. Finally, the different analog circuits on which machine learning techniques were applied are also presented and their results discussed from a circuit designer perspective.https://www.mdpi.com/2079-9292/11/3/435automated circuit sizingmachine learninganalog IC designdeep neural networks |
| spellingShingle | Rayan Mina Chadi Jabbour George E. Sakr A Review of Machine Learning Techniques in Analog Integrated Circuit Design Automation Electronics automated circuit sizing machine learning analog IC design deep neural networks |
| title | A Review of Machine Learning Techniques in Analog Integrated Circuit Design Automation |
| title_full | A Review of Machine Learning Techniques in Analog Integrated Circuit Design Automation |
| title_fullStr | A Review of Machine Learning Techniques in Analog Integrated Circuit Design Automation |
| title_full_unstemmed | A Review of Machine Learning Techniques in Analog Integrated Circuit Design Automation |
| title_short | A Review of Machine Learning Techniques in Analog Integrated Circuit Design Automation |
| title_sort | review of machine learning techniques in analog integrated circuit design automation |
| topic | automated circuit sizing machine learning analog IC design deep neural networks |
| url | https://www.mdpi.com/2079-9292/11/3/435 |
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