Machine Learning-based feasibility estimation of digital blocks in BCD technology

Analog-on-Top Mixed Signal (AMS) Integrated Circuit (IC) design is a time-consuming process predominantly carried out by hand. Within this flow, usually, some area is reserved by the top-level integrator for the placement of digital blocks. Specific features of the area, such as size and shape, have...

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Bibliographic Details
Published in:arXiv.org 2024-10
Main Authors: Faraone, Gabriele, Daghero, Francesco, Serianni, Eugenio, Licastro, Dario, Nicola Di Carolo, Grosso, Michelangelo, Franchino, Giovanna Antonella, Pagliari, Daniele Jahier
Format: Article
Language:English
Subjects:
Analog circuits
Feasibility studies
Integrated circuits
Machine learning
Placement
Online Access:Get full text
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Summary:Analog-on-Top Mixed Signal (AMS) Integrated Circuit (IC) design is a time-consuming process predominantly carried out by hand. Within this flow, usually, some area is reserved by the top-level integrator for the placement of digital blocks. Specific features of the area, such as size and shape, have a relevant impact on the possibility of implementing the digital logic with the required functionality. We present a Machine Learning (ML)-based evaluation methodology for predicting the feasibility of digital implementation using a set of high-level features. This approach aims to avoid time-consuming Place-and-Route trials, enabling rapid feedback between Digital and Analog Back-End designers during top-level placement.
ISSN:2331-8422