Machine Learning-based feasibility estimation of digital blocks in BCD technology
<|reference_start|>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 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.<|reference_end|>
@article{faraone2024machine,
    title={Machine Learning-based feasibility estimation of digital blocks in BCD
  technology},
    author={Gabriele Faraone, Francesco Daghero, Eugenio Serianni, Dario Licastro,
  Nicola Di Carolo, Michelangelo Grosso, Giovanna Antonella Franchino, Daniele
  Jahier Pagliari},
    journal={arXiv preprint arXiv:2410.07989},
    year={2024},
    archivePrefix={arXiv},
    eprint={2410.07989},
    primaryClass={cs.LG}
}