• Bindushree V
• R Jaya Gowri

In order to change the entire chip design methodology, the Very Large-Scale Integration (VLSI) sector has begun implementing machine learning (ML) techniques in design automation. The time and effort required to comprehend and process the data within and across different abstraction levels via automated learning algorithms is reduced when machine learning techniques, such as reinforcement learning, are used in VLSI design and placement. It thus increases IC yield, shortens production lead times, and ensures accurate placement. In light of this, a thorough examination of numerous Reinforcement Learning-related aspects in the context of VLSI placement has been carried out. We have also considered the recent machine learning and deep learning techniques incorporated in VLSI.

Reinforcement learning, machine learning, algorithms, placement, design.

"Implementation Of Machine Learning In The Field Of VLSI Placement ", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.10, Issue 2, page no.d112-d116, February-2023, Available :http://www.jetir.org/papers/JETIR2302311.pdf

"Implementation Of Machine Learning In The Field Of VLSI Placement ", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.10, Issue 2, page no. ppd112-d116, February-2023, Available at : http://www.jetir.org/papers/JETIR2302311.pdf