DESIGN AUTOMATION FOR CARBON NANOTUBE CIRCUITS CONSIDERING PERFORMANCE AND SECURITY OPTIMIZATION

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. Buffer insertion for CNT interconnects is capable of improving circuit timing of signal nets with limited buffer deployment. However, due to the imperfection of fabricating long straight CNT, there exist significant unidimensional-spatially correlated variations on the critical CNT geometric parameters such as the diameter and density, which will affect the circuit performance. This dissertation develops a novel timing driven buffer 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

VLSI Design

This book provides some recent advances in design nanometer VLSI chips. The selected topics try to present some open problems and challenges with important topics ranging from design tools, new post-silicon devices, GPU-based parallel computing, emerging 3D integration, and antenna design. The book consists of two parts, with chapters such as: VLSI design for multi-sensor smart systems on a chip, Three-dimensional integrated circuits design for thousand-core processors, Parallel symbolic analysis of large analog circuits on GPU platforms, Algorithms for CAD tools VLSI design, A multilevel memetic algorithm for large SAT-encoded problems, etc

Appropriateness of Imperfect CNFET Based Circuits for Error Resilient Computing Systems

With superior device performance consistently reported in extremely scaled dimensions, low dimensional materials (LDMs), including Carbon Nanotube Field Effect Transistor (CNFET) based technology, have shown the potential to outperform silicon for future transistors in advanced technology nodes. Studies have also demonstrated orders of magnitude improvement in energy efficiency possible with LDMs, in comparison to silicon at competing technology nodes. However, the current fabrication processes for these materials suffer from process imperfections and still appear to be inadequate to compete with silicon for the mainstream high volume manufacturing. Among the LDMs, CNFETs are the most widely studied and closest to high volume manufacturing. Recent works have shown a significant increase in the complexity of CNFET based systems, including demonstration of a 16-bit microprocessor. However, the design of such systems has involved significantly wider-than-usual transistors and avoidance of certain logic combinations. The resulting complexity of several thousand transistors in such systems is still far from the requirements of high-performance general-purpose computing systems having billions of transistors. With the current progress of the process to fabricate CNFETs, their introduction in mainstream manufacturing is expected to take several more years. For an earlier technology adoption, CNFETs appear to be suited for error-resilient computing systems where errors during computation can be tolerated to a certain degree. Such systems relax the need for precise circuits and a perfect process while leveraging the potential energy benefits of CNFET technology in comparison to conventional Si technology. In this thesis, we explore the potential applications using an imperfect CNFET process for error-resilient computing systems, including the impact of the process imperfections at the system level and methods to improve it. The current most widely adopted fabrication process for CNFETs (separation and placement of solution-based CNTs) still suffers from process imperfections, mainly from open CNTs due to missing of CNTs (in trenches connecting source and drain of CNFET). A fair evaluation of the performance of CNFET based circuits should thus take into consideration the effect of open CNTs, resulting in reduced drive currents. At the circuit level, this leads to failures in meeting 1) the minimum frequency requirement (due to an increase in critical path delay), and 2) the noise suppression requirement. We present a methodology to accurately capture the effect of open CNT imperfection in the state-of-the-art CNFET model, for circuit-level performance evaluation (both delay and glitch vulnerability) of CNFET based circuits using SPICE. A Monte Carlo simulation framework is also provided to investigate the statistical effect of open CNT imperfection on circuit-level performance. We introduce essential metrics to evaluate glitch vulnerability and also provide an effective link between glitch vulnerability and circuit topology. The past few years have observed significant growth of interest in approximate computing for a wide range of applications, including signal processing, data mining, machine learning, image, video processing, etc. In such applications, the result quality is not compromised appreciably, even in the presence of few errors during computation. The ability to tolerate few errors during computation relaxes the need to have precise circuits. Thus the approximate circuits can be designed, with lesser nodes, reduced stages, and reduced capacitance at few nodes. Consequently, the approximate circuits could reduce critical path delays and enhanced noise suppression in comparison to precise circuits. We present a systematic methodology utilizing Reduced Ordered Binary Decision Diagrams (ROBDD) for generating approximate circuits by taking an example of 16-bit parallel prefix CNFET adder. The approximate adder generated using the proposed algorithm has ~ 5x reduction in the average number of nodes failing glitch criteria (along paths to primary output) and 43.4% lesser Energy Delay Product (EDP) even at high open CNT imperfection, in comparison to the ideal case of no open CNT imperfection, at a mean relative error of 3.3%. The recent boom of deep learning has been made possible by VLSI technology advancement resulting in hardware systems, which can support deep learning algorithms. These hardware systems intend to satisfy the high-energy efficiency requirement of such algorithms. The hardware supporting such algorithms adopts neuromorphic-computing architectures with significantly less energy compared to traditional Von Neumann architectures. Deep Neural Networks (DNNs) belonging to deep learning domain find its use in a wide range of applications such as image classification, speech recognition, etc. Recent hardware systems have demonstrated the implementation of complex neural networks at significantly less power. However, the complexity of applications and depths of DNNs are expected to drastically increase in the future, imposing a demanding requirement in terms of scalability and energy efficiency of hardware technology. CNFET technology can be an excellent alternative to meet the aggressive energy efficiency requirement for future DNNs. However, degradation in circuit-level performance due to open CNT imperfection can result in timing failure, thus distorting the shape of non-linear activation function, leading to a significant degradation in classification accuracy. We present a framework to obtain sigmoid activation function considering the effect of open CNT imperfection. A digital neuron is explored to generate the sigmoid activation function, which deviates from the ideal case under imperfect process and reduced time period (increased clock frequency). The inherent error resilience of DNNs, on the other hand, can be utilized to mitigate the impact of imperfect process and maintain the shape of the activation function. We use pruning of synaptic weights, which, combined with the proposed approximate neuron, significantly reduces the chance of timing failures and helps to maintain the activation function shape even at high process imperfection and higher clock frequencies. We also provide a framework to obtain classification accuracy of Deep Belief Networks (class of DNNs based on unsupervised learning) using the activation functions obtained from SPICE simulations. By using both approximate neurons and pruning of synaptic weights, we achieve excellent system accuracy (only < 0.5% accuracy drop) with 25% improvement in speed, significant EDP advantage (56.7% less) even at high process imperfection, in comparison to a base configuration of the precise neuron and no pruning with the ideal process, at no area penalty. In conclusion, this thesis provides directions for the potential applicability of CNFET based technology for error-resilient computing systems. For this purpose, we present methodologies, which provide approaches to assess and design CNFET based circuits, considering process imperfections. We accomplish a DBN framework for digit recognition, considering activation functions from SPICE simulations incorporating process imperfections. We demonstrate the effectiveness of using approximate neuron and synaptic weight pruning to mitigate the impact of high process imperfection on system accuracy

A Review Of Implementing Adc In Rfid Sensor

Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)The general considerations to design a sensor interface for passive RFID tags are discussed. This way, power and timing constraints imposed by ISO/IEC 15693 and ISO/IEC 14443 standards to HF RFID tags are explored. A generic multisensor interface is proposed and a survey analysis on the most suitable analog-to-digital converters for passive RFID sensing applications is reported. The most appropriate converter type and architecture are suggested. At the end, a specific sensor interface for carbon nanotube gas sensors is proposed and a brief discussion about its implemented circuits and preliminary results is made.Region Rhone-Alpes (France)CNPq (Brazil)INCT/NAMITEC (Brazil)Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq

A Review of Implementing ADC in RFID Sensor

The general considerations to design a sensor interface for passive RFID tags are discussed. This way, power and timing constraints imposed by ISO/IEC 15693 and ISO/IEC 14443 standards to HF RFID tags are explored. A generic multisensor interface is proposed and a survey analysis on the most suitable analog-to-digital converters for passive RFID sensing applications is reported. The most appropriate converter type and architecture are suggested. At the end, a specific sensor interface for carbon nanotube gas sensors is proposed and a brief discussion about its implemented circuits and preliminary results is made

A Review Of Implementing Adc In Rfid Sensor

Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)The general considerations to design a sensor interface for passive RFID tags are discussed. This way, power and timing constraints imposed by ISO/IEC 15693 and ISO/IEC 14443 standards to HF RFID tags are explored. A generic multisensor interface is proposed and a survey analysis on the most suitable analog-to-digital converters for passive RFID sensing applications is reported. The most appropriate converter type and architecture are suggested. At the end, a specific sensor interface for carbon nanotube gas sensors is proposed and a brief discussion about its implemented circuits and preliminary results is made.Region Rhone-Alpes (France)CNPq (Brazil)INCT/NAMITEC (Brazil)Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq

Interconnects for future technology generations - conventional CMOS with copper/low-k and beyond

The limitations of the conventional Cu/low-k interconnect technology for use in future ultra-scaled integrated circuits down to 7 nm in the year 2020 are investigated from the power/performance point of view. Compact models are used to demonstrate the impacts of various interconnect process parameters, for instance, the interconnect barrier/liner bilayer thickness and aspect ratio, on the design and optimization of a multilevel interconnect network. A framework to perform a sensitivity analysis for the circuit behavior to interconnect process parameters is created for future FinFET CMOS technology nodes. Multiple predictive cell libraries down to the 7‒nm technology node are constructed to enable early investigation of the electronic chip performance using commercial electronic design automation (EDA) tools with real chip information. Findings indicated new opportunities that arise for emerging novel interconnect technologies from the materials and process perspectives. These opportunities are evaluated based on potential benefits that are quantified with rigorous circuit-level simulations and requirements for key parameters are underlined. The impacts of various emerging interconnect technologies on the performances of emerging devices are analyzed to quantify the realistic circuit- and system-level benefits that these new switches can offer.Ph.D

NASA Tech Briefs, August 2003

Topics covered include: Stable, Thermally Conductive Fillers for Bolted Joints; Connecting to Thermocouples with Fewer Lead Wires; Zipper Connectors for Flexible Electronic Circuits; Safety Interlock for Angularly Misdirected Power Tool; Modular, Parallel Pulse-Shaping Filter Architectures; High-Fidelity Piezoelectric Audio Device; Photovoltaic Power Station with Ultracapacitors for Storage; Time Analyzer for Time Synchronization and Monitor of the Deep Space Network; Program for Computing Albedo; Integrated Software for Analyzing Designs of Launch Vehicles; Abstract-Reasoning Software for Coordinating Multiple Agents; Software Searches for Better Spacecraft-Navigation Models; Software for Partly Automated Recognition of Targets; Antistatic Polycarbonate/Copper Oxide Composite; Better VPS Fabrication of Crucibles and Furnace Cartridges; Burn-Resistant, Strong Metal-Matrix Composites; Self-Deployable Spring-Strip Booms; Explosion Welding for Hermetic Containerization; Improved Process for Fabricating Carbon Nanotube Probes; Automated Serial Sectioning for 3D Reconstruction; and Parallel Subconvolution Filtering Architectures