Empowering Analog Integrated Circuit Design through Large Language Models and Reinforcement Learning
Analog Integrated Circuit design consists of several complex steps that are difficult to optimize. Automating the transistor sizing process specifically comes with many challenges. The problem has a large design space, requires complex performance trade-offs, and needs to adjust to rapidly advancing...
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| Format: | Thesis |
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Massachusetts Institute of Technology
2024
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| Online Access: | https://hdl.handle.net/1721.1/156800 |