Paul Franzon
paulf@ncsu.edu
Monteith Research Center (MRC) 443
Campus Box 7911
Website
YouTube Channel
Biography
My teaching and research focuses on building microsystems (systems constructed of silicon chips, both analog and digital, and silicon micomachined components) for applications in computing, communications, sensors, robotics, and signal processing.
Education
-
Ph.D.
1988
Electrical Engineering
University of Adelaide, Australia -
Bachelor's
1984
Electrical Engineering
University of Adelaide, Australia -
Bachelor's
1982
Physics and Mathematics
University of Adelaide, Australia
Funded Research
- High Dimensional Inverse Methods for Electronic System Design, Center for Advanced Electronics Through Machine Learning (CAEML) Core Project
- GAN generated models for Signal Integrity, Thermal Modeling, Etc., Center for Advanced Electronics through Machine Learning (CAEML) Core Project 5A3
- Cirrus Logic Mixed Signal IC Design Fellowship
- FPGA Hardware Accelerator for Real Time Security, CAEML Core Project
- Differential Power Analysis of Deep Neural Networks with Mitigation at the Architecture Level
- Three Dimensional Study of the Benefits of Hybrid Bonding
- High-Dimensional Structural Inference for Non-Linear Deep Markov or State Space Time Series Models, Center for Advanced Electronics through Machine Learning (CAEML) Core Project 3A4
- Fast, Accurate PPA Model-Extraction, Center for Advanced Electronics through Machine Learning (CAEML) Core Project 3A2 funded with industry membership dues.
- Applying Machine Learning to Back End IC Design, Center for Advanced Electronics through Machine Learning (CAEML) Core Project 2A7 funded with industry membership dues.
- Trusted Fabrication through 3D Integration Demonstration
- 2.5D Extendible Processor
- RER-3D: A Research Center for Radiation Effects Reliability Mechanisms Unique to 3D Integration (Radiation Effects in 3D Structures)
- Intellectual Property Reuse through Machine Learning, Center for Advanced Electronics through Machine Learning (CAEML) Core Project 1A2 funded with industry membership dues.
- Behavioral Modeling for High Speed Links, Center for Advanced Electronics through Machine Learning (CAEML) Core Project 1A5 funded with industry membership dues.
- SHF:Small:AC Powered Digital Circuits
- Modular, Testable, Tightly Coupled 3D Implementation of a Heterogenous Processor
Highlighted Awards
Awards & Honors
- 2007 - Australian Defence Medal
- 2007, 2005 - ECE Graduate Teacher of the Year Award (NC State)
- 2007 - ECE Most Helpful Teacher of the Year Award (NC State)
- 2006 - ECE Teacher of the Year Award (NC State)
- 2006 - IEEE Fellow
- 2005 - ALCOA Distinguished Research Award (NC State)
- 2003 - Alumni Undergraduate Distinguished Professor (NC State)
- 2003 - NSW Australia Expatriate Scientist Award
- 2001 - Academy of Outstanding Teachers (NC State)
- 1997 - IEEE Student Branch, Teacher of the Year (NC State)
- 1993 - NSF Young Investigators Award
- 1990 - US Army Commendation Medal
Recent News
Franzon Selected for Qualcomm Faculty Award
Posted on May 21, 2021 | Filed Under: Faculty
Dr. Paul Franzon has been awarded a 2021 Qualcomm Faculty Awards for research that is inspirational to graduate students and serves to spark new approaches in key technology areas.
Smaller Chips Open Door to New RFID Applications
Posted on May 17, 2021 | Filed Under: Research
Researchers have made what is believed to be the smallest state-of-the-art RFID chip.
Dai and Franzon win 2019 Qualcomm Faculty Award
Posted on July 22, 2019 | Filed Under: Awards
Dr. Huaiyu Dai and Dr. Paul Franzon have been awarded 2019 Qualcomm Faculty Awards for research that is inspirational to graduate students and serves to spark new approaches in key technology areas.
Recent Media Mentions
AI Expands Role in Design
January 31, 2018
Machine Learning Offers Helping Hand To Edit Chips
July 6, 2017
It might not fit electronic design automation at all, said Paul Franzon, a professor of electrical and computer engineering at North Carolina State University.
New NCSU research leads to 25% smaller RFID tags
May 9, 2016
Engineering researchers at North Carolina State University have developed a suite of techniques that allow them to create passive radio-frequency identification (RFID) tags that are 25 percent smaller and therefore less expensive. Dr Paul Franzon featured.
Recent Publications
- 2Deep: Enhancing Side-Channel Attacks on Lattice-Based Key-Exchange via 2-D Deep Learning (2021)
- A 125 mu m x 245 mu m Mainly Digital UHF EPC Gen2 Compatible RFID Tag in 55 nm CMOS Process (2021)
- Application of Quantum Machine Learning to VLSI Placement (2020)
- Asymmetric Transformer Design With Multiband Frequency Response for Simultaneous Power and Data Transfer (2020)
- CTLE Adaptation Using Deep Learning in High-speed SerDes Link (2020)
- Design Rule Checking with a CNN Based Feature Extractor (2020)
- Machine Learning and Hardware security: Challenges and Opportunities -Invited Talk (2020)
- Multi-Fidelity Surrogate-Based Optimization for Electromagnetic Simulation Acceleration (2020)
- Self-Evolution Cascade Deep Learning Model for High-Speed Receiver Adaptation (2020)
- 3-D-DATE: A Circuit-Level Three-Dimensional DRAM Area, Timing, and Energy Model (2019)