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

arXiv: https://arxiv.org/abs/2410.07989 · HTML: https://mlsanity.com/paper/2410.07989/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
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- First posted on arXiv 10 Oct 2024.

## Abstract (arXiv)

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.