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
Main Authors: Daghero, Francesco, Faraone, Gabriele, Grosso, Michelangelo, Pagliari, Daniele Jahier, Di Carolo, Nicola, Franchino, Giovanna Antonella, Licastro, Dario, Serianni, Eugenio
Format: Conference Proceeding
Language:English
Subjects:
analog-mixed-signal
artificial intelligence
Decision trees
Design methodology
EDA
Integrated circuits
layout
Logic
machine learning
Manufacturing
Maximum likelihood estimation
Place and Route
Predictive models
Shape
Silicon
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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:2832-823X
DOI:10.1109/DTTIS62212.2024.10780062