Luis Francisco
College of Engineering
Works (5)
Updated: July 5th, 2023 15:07
2023 journal article
A Deep Transfer Learning Design Rule Checker With Synthetic Training
IEEE DESIGN & TEST, 40(1), 77–84.
author keywords: Layout; Design methodology; Convolutional neural networks; Transfer learning; Generators; Deep learning; Manuals; Training data; Design Rule Checking; Machine Learning; IC Verification; Physical Verification; Convolutional Neural Network; Deep Learning; Synthetic Data Training; Transfer Learning
Sources: Web Of Science, ORCID
Added: January 24, 2023
2021 article
Fast and Accurate PPA Modeling with Transfer Learning
2021 ACM/IEEE 3RD WORKSHOP ON MACHINE LEARNING FOR CAD (MLCAD).
author keywords: PPA; Machine Learning; Power; Performance; Area; Gradient Boost; Neural Network; Transfer Learning
TL;DR:
This work presents a machine learning approach using gradient boost models and neural networks to fast and accurately predict the power, performance, and area of a System-on-Chip (SoC) by reducing the number of samples used to create the models.
(via Semantic Scholar)
Sources: Web Of Science, ORCID
Added: November 15, 2021
2021 conference paper
Fast and Accurate PPA Modeling with Transfer Learning
2021 IEEE/ACM International Conference On Computer Aided Design (ICCAD). Presented at the 2021 IEEE/ACM International Conference On Computer Aided Design (ICCAD), Munich, Germany.
Event: 2021 IEEE/ACM International Conference On Computer Aided Design (ICCAD) at Munich, Germany on November 1-4, 2021
author keywords: PPA; Machine Learning; Power; Performance; Area; Gradient Boost; Neural Network; Transfer Learning; Surrogate Modeling
TL;DR:
The approach reached the same PPA solution as human designers in the same or fewer runs for a CORTEX-M0 system design, showing potential for automating the recipe optimization without needing more runs than a human designer would need.
(via Semantic Scholar)
UN Sustainable Development Goal Categories
3. Good Health and Well-being
(Web of Science)
9. Industry, Innovation and Infrastructure
(OpenAlex)
Sources: Web Of Science, ORCID
Added: February 21, 2022
2020 article
Design Rule Checking with a CNN Based Feature Extractor
PROCEEDINGS OF THE 2020 ACM/IEEE 2ND WORKSHOP ON MACHINE LEARNING FOR CAD (MLCAD '20), pp. 9–14.
author keywords: Design Rule Checking; Machine Learning; IC Verification; Design for Manufacturing; Convolutional Neural Network; Deep Learning
TL;DR:
The proof of feasibility for a fast interactive DRC engine that could be used during layout is established and the proposed model consists of a convolutional neural network trained to detect DRC violations.
(via Semantic Scholar)
arxiv.org (repository)
UN Sustainable Development Goal Categories
9. Industry, Innovation and Infrastructure
(Web of Science)
Sources: Web Of Science, ORCID
Added: August 16, 2021
2019 article
Multilayer CMP Hotspot Modeling Through Deep Learning
DESIGN-PROCESS-TECHNOLOGY CO-OPTIMIZATION FOR MANUFACTURABILITY XIII, Vol. 10962.
author keywords: CMP; Machine Learning; DFM; Advanced Lithography; Depth of Focus; CMP hotspots; Chip Topography
TL;DR:
A Deep Learning (DL) multilayer convolutional neural network (CNN) algorithm is used to model CMP hotspots for full-chip multilayers layouts enabling modeling and prediction of hotspots resulting from complex inter-layer interactions or effects which may escape traditional methods.
(via Semantic Scholar)
UN Sustainable Development Goal Categories
9. Industry, Innovation and Infrastructure
(Web of Science)
Source: Web Of Science
Added: November 11, 2019
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