Dissertation/Thesis Abstract

Design Automation for Carbon Nanotube Circuits Considering Performance and Security Optimization
by Liu, Lin, Ph.D., Michigan Technological University, 2017, 169; 10267186
Abstract (Summary)

As prevailing copper interconnect technology advances to its fundamental physical limit, interconnect delay due to ever-increasing wire resistivity has greatly limited the circuit miniaturization. Carbon nanotube (CNT) interconnects have emerged as promising replacement materials for copper interconnects due to their superior conductivity. Buer insertion for CNT interconnects is capable of improving circuit timing of signal nets with limited buer deployment. However, due to the imperfection of fabricating long straight CNT, there exist signicant unidimensional-spatially correlated variations on the critical CNT geometric parameters such as the diameter and density, which will aect the circuit performance. This dissertation develops a novel timing driven buer insertion technique considering unidimensional correlations of variations of CNT. Although the fabrication variations of CNTs are not desired for the circuit designs targeting performance optimization and reliability, these inherent imperfections make them natural candidates for building highly secure physical unclonable function (PUF), which is an advanced hardware security technology. A novel CNT PUF design through leveraging Lorenz chaotic system is developed and we show that it is resistant to many machine learning modeling attacks. In summary, the studies in this dissertation demonstrate that CNT technology is highly promising for performance and security optimizations in advanced VLSI circuit design.

Indexing (document details)
Advisor: Hu, Shiyan
Commitee: Onder, Nilufer, Sun, Ye, Wang, Zhaohui
School: Michigan Technological University
Department: Electrical and Computer Engineering
School Location: United States -- Michigan
Source: DAI-B 78/10(E), Dissertation Abstracts International
Source Type: DISSERTATION
Subjects: Computer Engineering
Keywords: Buffer insertion, Carbon nanotubes, Machine learning, Physical unclonable function, Security, Timing optimization
Publication Number: 10267186
ISBN: 9781369822281
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