Luis Francisco

College of Engineering

Works (5)

Updated: July 5th, 2023 15:07

2022 journal article

A Deep Transfer Learning Design Rule Checker With Synthetic Training

IEEE DESIGN & TEST, 40(1), 77–84.

By: L. Francisco n, W. Davis & P. Franzon n 

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, NC State University Libraries
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).

By: L. Francisco n, P. Franzon n  & W. Davis  n

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)
UN Sustainable Development Goal Categories
Sources: Web Of Science, NC State University Libraries
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.

By: W. Davis n , P. Franzon n , L. Francisco n, B. Huggins n & R. Jain *

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
9. Industry, Innovation and Infrastructure (OpenAlex)
Sources: Web Of Science, NC State University Libraries
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.

By: L. Francisco n, T. Lagare n, A. Jain  n, S. Chaudhary n, M. Kulkarni  n, D. Sardana n, W. Davis  n, P. Franzon n 

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)
UN Sustainable Development Goal Categories
Sources: Web Of Science, NC State University Libraries
Added: August 16, 2021

2019 article

Multilayer CMP Hotspot Modeling Through Deep Learning

DESIGN-PROCESS-TECHNOLOGY CO-OPTIMIZATION FOR MANUFACTURABILITY XIII, Vol. 10962.

By: L. Francisco n, R. Mao *, U. Katakamsetty*, P. Verma * & R. Pack *

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
Source: Web Of Science
Added: November 11, 2019

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