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

Design and modelling of variability tolerant on-chip communication structures for future high performance system on chip designs

The incessant technology scaling has enabled the integration of functionally complex System-on-Chip (SoC) designs with a large number of heterogeneous systems on a single chip. The processing elements on these chips are integrated through on-chip communication structures which provide the infrastructure necessary for the exchange of data and control signals, while meeting the strenuous physical and design constraints. The use of vast amounts of on chip communications will be central to future designs where variability is an inherent characteristic. For this reason, in this thesis we investigate the performance and variability tolerance of typical on-chip communication structures. Understanding of the relationship between variability and communication is paramount for the designers; i.e. to devise new methods and techniques for designing performance and power efficient communication circuits in the forefront of challenges presented by deep sub-micron (DSM) technologies. The initial part of this work investigates the impact of device variability due to Random Dopant Fluctuations (RDF) on the timing characteristics of basic communication elements. The characterization data so obtained can be used to estimate the performance and failure probability of simple links through the methodology proposed in this work. For the Statistical Static Timing Analysis (SSTA) of larger circuits, a method for accurate estimation of the probability density functions of different circuit parameters is proposed. Moreover, its significance on pipelined circuits is highlighted. Power and area are one of the most important design metrics for any integrated circuit (IC) design. This thesis emphasises the consideration of communication reliability while optimizing for power and area. A methodology has been proposed for the simultaneous optimization of performance, area, power and delay variability for a repeater inserted interconnect. Similarly for multi-bit parallel links, bandwidth driven optimizations have also been performed. Power and area efficient semi-serial links, less vulnerable to delay variations than the corresponding fully parallel links are introduced. Furthermore, due to technology scaling, the coupling noise between the link lines has become an important issue. With ever decreasing supply voltages, and the corresponding reduction in noise margins, severe challenges are introduced for performing timing verification in the presence of variability. For this reason an accurate model for crosstalk noise in an interconnection as a function of time and skew is introduced in this work. This model can be used for the identification of skew condition that gives maximum delay noise, and also for efficient design verification

Architectural level delay and leakage power modelling of manufacturing process variation

PhD ThesisThe effect of manufacturing process variations has become a major issue regarding the estimation of circuit delay and power dissipation, and will gain more importance in the future as device scaling continues in order to satisfy market place demands for circuits with greater performance and functionality per unit area. Statistical modelling and analysis approaches have been widely used to reflect the effects of a variety of variational process parameters on system performance factor which will be described as probability density functions (PDFs). At present most of the investigations into statistical models has been limited to small circuits such as a logic gate. However, the massive size of present day electronic systems precludes the use of design techniques which consider a system to comprise these basic gates, as this level of design is very inefficient and error prone. This thesis proposes a methodology to bring the effects of process variation from transistor level up to architectural level in terms of circuit delay and leakage power dissipation. Using a first order canonical model and statistical analysis approach, a statistical cell library has been built which comprises not only the basic gate cell models, but also more complex functional blocks such as registers, FIFOs, counters, ALUs etc. Furthermore, other sensitive factors to the overall system performance, such as input signal slope, output load capacitance, different signal switching cases and transition types are also taken into account for each cell in the library, which makes it adaptive to an incremental circuit design. The proposed methodology enables an efficient analysis of process variation effects on system performance with significantly reduced computation time compared to the Monte Carlo simulation approach. As a demonstration vehicle for this technique, the delay and leakage power distributions of a 2-stage asynchronous micropipeline circuit has been simulated using this cell library. The experimental results show that the proposed method can predict the delay and leakage power distribution with less than 5% error and at least 50,000 times faster computation time compare to 5000-sample SPICE based Monte Carlo simulation. The methodology presented here for modelling process variability plays a significant role in Design for Manufacturability (DFM) by quantifying the direct impact of process variations on system performance. The advantages of being able to undertake this analysis at a high level of abstraction and thus early in the design cycle are two fold. First, if the predicted effects of process variation render the circuit performance to be outwith specification, design modifications can be readily incorporated to rectify the situation. Second, knowing what the acceptable limits of process variation are to maintain design performance within its specification, informed choices can be made regarding the implementation technology and manufacturer selected to fabricate the design

Comparing the impact of power supply voltage on CMOS-and FinFET-based SRAMs in the presence of resistive defects

CMOS technology scaling has reached its limit at the 22 nm technology node due to several factors including Process Variations (PV), increased leakage current, Random Dopant Fluctuation (RDF), and mainly the Short-Channel Effect (SCE). In order to continue the miniaturization process via technology down-scaling while preserving system reliability and performance, Fin Field-Effect Transistors (FinFETs) arise as an alternative to CMOS transistors. In parallel, Static Random-Access Memories (SRAMs) increasingly occupy great part of Systems-on-Chips’ (SoCs) silicon area, making their reliability an important issue. SRAMs are designed to reach densities at the limit of the manufacturing process, making this component susceptible to manufacturing defects, including the resistive ones. Such defects may cause dynamic faults during the circuits’ lifetime, an important cause of test escape. Thus, the identification of the proper faulty behavior taking different operating conditions into account is considered crucial to guarantee the development of more suitable test methodologies. In this context, a comparison between the behavior of a 22 nm CMOS-based and a 20 nm FinFET-based SRAM in the presence of resistive defects is carried out considering different power supply voltages. In more detail, the behavior of defective cells operating under different power supply voltages has been investigated performing SPICE simulations. Results show that the power supply voltage plays an important role in the faulty behavior of both CMOS- and FinFET-based SRAM cells in the presence of resistive defects but demonstrate to be more expressive when considering the FinFET-based memories. Studying different operating temperatures, the results show an expressively higher occurrence of dynamic faults in FinFET-based SRAMs when compared to CMOS technology

Open-access silicon photonics: current status and emerging initiatives

Silicon photonics is widely acknowledged as a game-changing technology driven by the needs of datacom and telecom. Silicon photonics builds on highly capital-intensive manufacturing infrastructure, and mature open-access silicon photonics platforms are translating the technology from research fabs to industrial manufacturing levels. To meet the current market demands for silicon photonics manufacturing, a variety of open-access platforms is offered by CMOS pilot lines, R&D institutes, and commercial foundries. This paper presents an overview of existing and upcoming commercial and noncommercial open-access silicon photonics technology platforms. We also discuss the diversity in these open-access platforms and their key differentiators

Statistical Characterization and Decomposition of SRAM cell Variability and Aging

abstract: Memories play an integral role in today's advanced ICs. Technology scaling has enabled high density designs at the price paid for impact due to variability and reliability. It is imperative to have accurate methods to measure and extract the variability in the SRAM cell to produce accurate reliability projections for future technologies. This work presents a novel test measurement and extraction technique which is non-invasive to the actual operation of the SRAM memory array. The salient features of this work include i) A single ended SRAM test structure with no disturbance to SRAM operations ii) a convenient test procedure that only requires quasi-static control of external voltages iii) non-iterative method that extracts the VTH variation of each transistor from eight independent switch point measurements. With the present day technology scaling, in addition to the variability with the process, there is also the impact of other aging mechanisms which become dominant. The various aging mechanisms like Negative Bias Temperature Instability (NBTI), Channel Hot Carrier (CHC) and Time Dependent Dielectric Breakdown (TDDB) are critical in the present day nano-scale technology nodes. In this work, we focus on the impact of NBTI due to aging in the SRAM cell and have used Trapping/De-Trapping theory based log(t) model to explain the shift in threshold voltage VTH. The aging section focuses on the following i) Impact of Statistical aging in PMOS device due to NBTI dominates the temporal shift of SRAM cell ii) Besides static variations , shifting in VTH demands increased guard-banding margins in design stage iii) Aging statistics remain constant during the shift, presenting a secondary effect in aging prediction. iv) We have investigated to see if the aging mechanism can be used as a compensation technique to reduce mismatch due to process variations. Finally, the entire test setup has been tested in SPICE and also validated with silicon and the results are presented. The method also facilitates the study of design metrics such as static, read and write noise margins and also the data retention voltage and thus help designers to improve the cell stability of SRAM.Dissertation/ThesisM.S. Electrical Engineering 201

Variability analysis of FinFET AC/RF performances through efficient physics-based simulations for the optimization of RF CMOS stages

A nearly insatiable appetite for the latest electronic device enables the electronic technology sector to maintain research momentum. The necessity for advancement with miniaturization of electronic devices is the need of the day. Aggressive downscaling of electronic devices face some fundamental limits and thus, buoy up the change in device geometry. MOSFETs have been the leading contender in the electronics industry for years, but the dire need for miniaturization is forcing MOSFET to be scaled to nano-scale and in sub-50 nm scale. Short channel effects (SCE) become dominant and adversely affect the performance of the MOSFET. So, the need for a novel structure was felt to suppress SCE to an acceptable level. Among the proposed devices, FinFETs (Fin Field Effect Transistors) were found to be most effective to counter-act SCE in electronic devices. Today, many industries are working on electronic circuits with FinFETs as their primary element.One of limitation which FinFET faces is device variability. The purpose of this work was to study the effect that different sources of parameter fluctuations have on the behavior and characteristics of FinFETs. With deep literature review, we have gained insight into key sources of variability. Different sources of variations, like random dopant fluctuation, line edge roughness, fin variations, workfunction variations, oxide thickness variation, and source/drain doping variations, were studied and their impact on the performance of the device was studied as well. The adverse effect of these variations fosters the great amount of research towards variability modeling. A proper modeling of these variations is required to address the device performance metric before the fabrication of any new generation of the device on the commercial scale. The conventional methods to address the characteristics of a device under variability are Monte-Carlo-like techniques. In Monte Carlo analysis, all process parameters can be varied individually or simultaneously in a more realistic approach. The Monte Carlo algorithm takes a random value within the range of each process parameter and performs circuit simulations repeatedly. The statistical characteristics are estimated from the responses. This technique is accurate but requires high computational resources and time. Thus, efforts are being put by different research groups to find alternative tools. If the variations are small, Green’s Function (GF) approach can be seen as a breakthrough methodology. One of the most open research fields regards "Variability of FinFET AC performances". One reason for the limited AC variability investigations is the lack of commercially available efficient simulation tools, especially those based on accurate physics-based analysis: in fact, the only way to perform AC variability analysis through commercial TCAD tools like Synopsys Sentaurus is through the so-called Monte Carlo approach, that when variations are deterministic, is more properly referred to as incremental analysis, i.e., repeated solutions of the device model with varying physical parameters. For each selected parameter, the model must be solved first in DC operating condition (working point, WP) and then linearized around the WP, hence increasing severely the simulation time. In this work, instead, we used GF approach, using our in-house Simulator "POLITO", to perform AC variability analysis, provided that variations are small, alleviating the requirement of double linearization and reducing the simulation time significantly with a slight trade-off in accuracy. Using this tool we have, for the first time addressed the dependency of FinFET AC parameters on the most relevant process variations, opening the way to its application to RF circuits. This work is ultimately dedicated to the successful implementation of RF stages in commercial applications by incorporating variability effects and controlling the degradation of AC parameters due to variability. We exploited the POLITO (in-house simulator) limited to 2D structures, but this work can be extended to the variability analysis of 3D FinFET structure. Also variability analysis of III-V Group structures can be addressed. There is also potentiality to carry out the sensitivity analysis for the other source of variations, e.g., thermal variations

Design of Variation-Tolerant Circuits for Nanometer CMOS Technology: Circuits and Architecture Co-Design

Aggressive scaling of CMOS technology in sub-90nm nodes has created huge challenges. Variations due to fundamental physical limits, such as random dopants fluctuation (RDF) and line edge roughness (LER) are increasing significantly with technology scaling. In addition, manufacturing tolerances in process technology are not scaling at the same pace as transistor's channel length due to process control limitations (e.g., sub-wavelength lithography). Therefore, within-die process variations worsen with successive technology generations. These variations have a strong impact on the maximum clock frequency and leakage power for any digital circuit, and can also result in functional yield losses in variation-sensitive digital circuits (such as SRAM). Moreover, in nanometer technologies, digital circuits show an increased sensitivity to process variations due to low-voltage operation requirements, which are aggravated by the strong demand for lower power consumption and cost while achieving higher performance and density. It is therefore not surprising that the International Technology Roadmap for Semiconductors (ITRS) lists variability as one of the most challenging obstacles for IC design in nanometer regime. To facilitate variation-tolerant design, we study the impact of random variations on the delay variability of a logic gate and derive simple and scalable statistical models to evaluate delay variations in the presence of within-die variations. This work provides new design insight and highlights the importance of accounting for the effect of input slew on delay variations, especially at lower supply voltages. The derived models are simple, scalable, bias dependent and only require the knowledge of easily measurable parameters. This makes them useful in early design exploration, circuit/architecture optimization as well as technology prediction (especially in low-power and low-voltage operation). The derived models are verified using Monte Carlo SPICE simulations using industrial 90nm technology. Random variations in nanometer technologies are considered one of the largest design considerations. This is especially true for SRAM, due to the large variations in bitcell characteristics. Typically, SRAM bitcells have the smallest device sizes on a chip. Therefore, they show the largest sensitivity to different sources of variations. With the drastic increase in memory densities, lower supply voltages and higher variations, statistical simulation methodologies become imperative to estimate memory yield and optimize performance and power. In this research, we present a methodology for statistical simulation of SRAM read access yield, which is tightly related to SRAM performance and power consumption. The proposed flow accounts for the impact of bitcell read current variation, sense amplifier offset distribution, timing window variation and leakage variation on functional yield. The methodology overcomes the pessimism existing in conventional worst-case design techniques that are used in SRAM design. The proposed statistical yield estimation methodology allows early yield prediction in the design cycle, which can be used to trade off performance and power requirements for SRAM. The methodology is verified using measured silicon yield data from a 1Mb memory fabricated in an industrial 45nm technology. Embedded SRAM dominates modern SoCs and there is a strong demand for SRAM with lower power consumption while achieving high performance and high density. However, in the presence of large process variations, SRAMs are expected to consume larger power to ensure correct read operation and meet yield targets. We propose a new architecture that significantly reduces array switching power for SRAM. The proposed architecture combines built-in self-test (BIST) and digitally controlled delay elements to reduce the wordline pulse width for memories while ensuring correct read operation; hence, reducing switching power. A new statistical simulation flow was developed to evaluate the power savings for the proposed architecture. Monte Carlo simulations using a 1Mb SRAM macro from an industrial 45nm technology was used to examine the power reduction achieved by the system. The proposed architecture can reduce the array switching power significantly and shows large power saving - especially as the chip level memory density increases. For a 48Mb memory density, a 27% reduction in array switching power can be achieved for a read access yield target of 95%. In addition, the proposed system can provide larger power saving as process variations increase, which makes it a very attractive solution for 45nm and below technologies. In addition to its impact on bitcell read current, the increase of local variations in nanometer technologies strongly affect SRAM cell stability. In this research, we propose a novel single supply voltage read assist technique to improve SRAM static noise margin (SNM). The proposed technique allows precharging different parts of the bitlines to VDD and GND and uses charge sharing to precisely control the bitline voltage, which improves the bitcell stability. In addition to improving SNM, the proposed technique also reduces memory access time. Moreover, it only requires one supply voltage, hence, eliminates the need of large area voltage shifters. The proposed technique has been implemented in the design of a 512kb memory fabricated in 45nm technology. Results show improvements in SNM and read operation window which confirms the effectiveness and robustness of this technique