Tagged repair techniques for defect tolerance in hybrid nano/CMOS architecture

We propose two new repair techniques for hybrid nano/CMOS computing architecture with lookup table based Boolean logic. Our proposed techniques use tagging mechanism to provide high level of defect tolerance and we present theoretical equations to predict the repair capability including an estimate of the repair cost. The repair techniques are efficient in utilization of spare units and capable of targeting upto 20% defect rates, which is higher than recently reported repair techniques

A survey of carbon nanotube interconnects for energy efficient integrated circuits

This article is a review of the state-of-art carbon nanotube interconnects for Silicon application with respect to the recent literature. Amongst all the research on carbon nanotube interconnects, those discussed here cover 1) challenges with current copper interconnects, 2) process & growth of carbon nanotube interconnects compatible with back-end-of-line integration, and 3) modeling and simulation for circuit-level benchmarking and performance prediction. The focus is on the evolution of carbon nanotube interconnects from the process, theoretical modeling, and experimental characterization to on-chip interconnect applications. We provide an overview of the current advancements on carbon nanotube interconnects and also regarding the prospects for designing energy efficient integrated circuits. Each selected category is presented in an accessible manner aiming to serve as a survey and informative cornerstone on carbon nanotube interconnects relevant to students and scientists belonging to a range of fields from physics, processing to circuit design

Improving the Fault Tolerance of Nanometric PLA Designs

Several alternative building blocks have been proposed to replace planar transistors, among which a prominent spot belongs to nanometric laments such as Silicon NanoWires (SiNWs) and Carbon NanoTubes (CNTs). However, chips leveraging these nanoscale structures are expected to be affected by a large amount of manufacturing faults, way beyond what chip architects have learned to counter. In this paper, we show a design ow, based on software mapping algorithms, to improve the yield of nanometric Programmable Logic Arrays (PLAs). While further improvements to the manufacturing technology will be needed to make these devices fully usable, our ow can signi cantly shrink the gap between current and desired yield levels. Also, our approach does not need post-fabrication functional analysis and mapping, therefore dramatically cutting on veri cation costs. We check PLA yields by means of an accurate analyzer after Monte Carlo fault injection. We show that, compared to a baseline policy of wire replication, we achieve equal or better yields (8% over a set of designs) depending on the underlying defect assumptions

AI/ML Algorithms and Applications in VLSI Design and Technology

An evident challenge ahead for the integrated circuit (IC) industry in the nanometer regime is the investigation and development of methods that can reduce the design complexity ensuing from growing process variations and curtail the turnaround time of chip manufacturing. Conventional methodologies employed for such tasks are largely manual; thus, time-consuming and resource-intensive. In contrast, the unique learning strategies of artificial intelligence (AI) provide numerous exciting automated approaches for handling complex and data-intensive tasks in very-large-scale integration (VLSI) design and testing. Employing AI and machine learning (ML) algorithms in VLSI design and manufacturing reduces the time and effort for understanding and processing the data within and across different abstraction levels via automated learning algorithms. It, in turn, improves the IC yield and reduces the manufacturing turnaround time. This paper thoroughly reviews the AI/ML automated approaches introduced in the past towards VLSI design and manufacturing. Moreover, we discuss the scope of AI/ML applications in the future at various abstraction levels to revolutionize the field of VLSI design, aiming for high-speed, highly intelligent, and efficient implementations

Variability-aware architectures based on hardware redundancy for nanoscale reliable computation

During the last decades, human beings have experienced a significant enhancement in the quality of life thanks in large part to the fast evolution of Integrated Circuits (IC). This unprecedented technological race, along with its significant economic impact, has been grounded on the production of complex processing systems from highly reliable compounding devices. However, the fundamental assumption of nearly ideal devices, which has been true within the past CMOS technology generations, today seems to be coming to an end. In fact, as MOSFET technology scales into nanoscale regime it approaches to fundamental physical limits and starts experiencing higher levels of variability, performance degradation, and higher rates of manufacturing defects. On the other hand, ICs with increasing number of transistors require a decrease in the failure rate per device in order to maintain the overall chip reliability. As a result, it is becoming increasingly important today the development of circuit architectures capable of providing reliable computation while tolerating high levels of variability and defect rates. The main objective of this thesis is to analyze and propose new fault-tolerant architectures based on redundancy for future technologies. Our research is founded on the principles of redundancy established by von Neumann in the 1950s and extends them to three new dimensions: 1. Heterogeneity: Most of the works on fault-tolerant architectures based on redundancy assume homogeneous variability in the replicas like von Neumann's original work. Instead, we explore the possibilities of redundancy when heterogeneity between replicas is taken into account. In this sense, we propose compensating mechanisms that select the weighting of the redundant information to maximize the overall reliability. 2. Asynchrony: Each of the replicas of a redundant system may have associated different processing delays due to variability and degradation; especially in future nanotechnologies. If we design our system to work locally in asynchronous mode then we may consider different voting policies to deal with the redundant information. Depending on how many replicas we collect before taking a decision we can obtain different trade-off between processing delay and reliability. We propose a mechanism for providing these facilities and analyze and simulate its operation. 3. Hierarchy: Finally, we explore the possibilities of redundancy applied at different hierarchy layers of complex processing systems. We propose to distribute redundancy across the various hierarchy layers and analyze the benefits that can be obtained. Drawing on the scenario of future ICs technologies, we push the concept of redundancy to its fullest expression through the study of realistic nano-device architectures. Most of the redundant architectures considered so far do not face properly the era of Terascale Computing and the nanotechnology trends. Since von Neumann applied for the first time redundancy at electronic circuits, never until now effects as common in nanoelectronics as degradation and interconnection failures have been treated directly from the standpoint of redundancy. In this thesis we address in a comprehensive manner the reliability of digital processing systems in the upcoming technology generations