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

Cross-Layer Resiliency Modeling and Optimization: A Device to Circuit Approach

The never ending demand for higher performance and lower power consumption pushes the VLSI industry to further scale the technology down. However, further downscaling of technology at nano-scale leads to major challenges. Reduced reliability is one of them, arising from multiple sources e.g. runtime variations, process variation, and transient errors. The objective of this thesis is to tackle unreliability with a cross layer approach from device up to circuit level

Robust Design of Variation-Sensitive Digital Circuits

The nano-age has already begun, where typical feature dimensions are smaller than 100nm. The operating frequency is expected to increase up to 12 GHz, and a single chip will contain over 12 billion transistors in 2020, as given by the International Technology Roadmap for Semiconductors (ITRS) initiative. ITRS also predicts that the scaling of CMOS devices and process technology, as it is known today, will become much more difficult as the industry advances towards the 16nm technology node and further. This aggressive scaling of CMOS technology has pushed the devices to their physical limits. Design goals are governed by several factors other than power, performance and area such as process variations, radiation induced soft errors, and aging degradation mechanisms. These new design challenges have a strong impact on the parametric yield of nanometer digital circuits and also result in functional yield losses in variation-sensitive digital circuits such as Static Random Access Memory (SRAM) and flip-flops. Moreover, sub-threshold SRAM and flip-flops circuits, which are aggravated by the strong demand for lower power consumption, show larger sensitivity to these challenges which reduces their robustness and yield. Accordingly, it is not surprising that the ITRS considers variability and reliability as the most challenging obstacles for nanometer digital circuits robust design. Soft errors are considered one of the main reliability and robustness concerns in SRAM arrays in sub-100nm technologies due to low operating voltage, small node capacitance, and high packing density. The SRAM arrays soft errors immunity is also affected by process variations. We develop statistical design-oriented soft errors immunity variations models for super-threshold and sub-threshold SRAM cells accounting for die-to-die variations and within-die variations. This work provides new design insights and highlights the important design knobs that can be used to reduce the SRAM cells soft errors immunity variations. The developed models are scalable, bias dependent, and only require the knowledge of easily measurable parameters. This makes them useful in early design exploration, circuit optimization as well as technology prediction. The derived models are verified using Monte Carlo SPICE simulations, referring to an industrial hardware-calibrated 65nm CMOS technology. The demand for higher performance leads to very deep pipelining which means that hundreds of thousands of flip-flops are required to control the data flow under strict timing constraints. A violation of the timing constraints at a flip-flop can result in latching incorrect data causing the overall system to malfunction. In addition, the flip-flops power dissipation represents a considerable fraction of the total power dissipation. Sub-threshold flip-flops are considered the most energy efficient solution for low power applications in which, performance is of secondary importance. Accordingly, statistical gate sizing is conducted to different flip-flops topologies for timing yield improvement of super-threshold flip-flops and power yield improvement of sub-threshold flip-flops. Following that, a comparative analysis between these flip-flops topologies considering the required overhead for yield improvement is performed. This comparative analysis provides useful recommendations that help flip-flops designers on selecting the best flip-flops topology that satisfies their system specifications while taking the process variations impact and robustness requirements into account. Adaptive Body Bias (ABB) allows the tuning of the transistor threshold voltage, Vt, by controlling the transistor body voltage. A forward body bias reduces Vt, increasing the device speed at the expense of increased leakage power. Alternatively, a reverse body bias increases Vt, reducing the leakage power but slowing the device. Therefore, the impact of process variations is mitigated by speeding up slow and less leaky devices or slowing down devices that are fast and highly leaky. Practically, the implementation of the ABB is desirable to bias each device in a design independently, to mitigate within-die variations. However, supplying so many separate voltages inside a die results in a large area overhead. On the other hand, using the same body bias for all devices on the same die limits its capability to compensate for within-die variations. Thus, the granularity level of the ABB scheme is a trade-off between the within-die variations compensation capability and the associated area overhead. This work introduces new ABB circuits that exhibit lower area overhead by a factor of 143X than that of previous ABB circuits. In addition, these ABB circuits are resolution free since no digital-to-analog converters or analog-to-digital converters are required on their implementations. These ABB circuits are adopted to high performance critical paths, emulating a real microprocessor architecture, for process variations compensation and also adopted to SRAM arrays, for Negative Bias Temperature Instability (NBTI) aging and process variations compensation. The effectiveness of the new ABB circuits is verified by post layout simulation results and test chip measurements using triple-well 65nm CMOS technology. The highly capacitive nodes of wide fan-in dynamic circuits and SRAM bitlines limit the performance of these circuits. In addition, process variations mitigation by statistical gate sizing increases this capacitance further and fails in achieving the target yield improvement. We propose new negative capacitance circuits that reduce the overall parasitic capacitance of these highly capacitive nodes. These negative capacitance circuits are adopted to wide fan-in dynamic circuits for timing yield improvement up to 99.87% and to SRAM arrays for read access yield improvement up to 100%. The area and power overheads of these new negative capacitance circuits are amortized over the large die area of the microprocessor and the SRAM array. The effectiveness of the new negative capacitance circuits is verified by post layout simulation results and test chip measurements using 65nm CMOS technology

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

Reliability in the face of variability in nanometer embedded memories

In this thesis, we have investigated the impact of parametric variations on the behaviour of one performance-critical processor structure - embedded memories. As variations manifest as a spread in power and performance, as a first step, we propose a novel modeling methodology that helps evaluate the impact of circuit-level optimizations on architecture-level design choices. Choices made at the design-stage ensure conflicting requirements from higher-levels are decoupled. We then complement such design-time optimizations with a runtime mechanism that takes advantage of adaptive body-biasing to lower power whilst improving performance in the presence of variability. Our proposal uses a novel fully-digital variation tracking hardware using embedded DRAM (eDRAM) cells to monitor run-time changes in cache latency and leakage. A special fine-grain body-bias generator uses the measurements to generate an optimal body-bias that is needed to meet the required yield targets. A novel variation-tolerant and soft-error hardened eDRAM cell is also proposed as an alternate candidate for replacing existing SRAM-based designs in latency critical memory structures. In the ultra low-power domain where reliable operation is limited by the minimum voltage of operation (Vddmin), we analyse the impact of failures on cache functional margin and functional yield. Towards this end, we have developed a fully automated tool (INFORMER) capable of estimating memory-wide metrics such as power, performance and yield accurately and rapidly. Using the developed tool, we then evaluate the #effectiveness of a new class of hybrid techniques in improving cache yield through failure prevention and correction. Having a holistic perspective of memory-wide metrics helps us arrive at design-choices optimized simultaneously for multiple metrics needed for maintaining lifetime requirements

Statistical circuit simulations - from ‘atomistic’ compact models to statistical standard cell characterisation

This thesis describes the development and application of statistical circuit simulation methodologies to analyse digital circuits subject to intrinsic parameter fluctuations. The specific nature of intrinsic parameter fluctuations are discussed, and we explain the crucial importance to the semiconductor industry of developing design tools which accurately account for their effects. Current work in the area is reviewed, and three important factors are made clear: any statistical circuit simulation methodology must be based on physically correct, predictive models of device variability; the statistical compact models describing device operation must be characterised for accurate transient analysis of circuits; analysis must be carried out on realistic circuit components. Improving on previous efforts in the field, we posit a statistical circuit simulation methodology which accounts for all three of these factors. The established 3-D Glasgow atomistic simulator is employed to predict electrical characteristics for devices aimed at digital circuit applications, with gate lengths from 35 nm to 13 nm. Using these electrical characteristics, extraction of BSIM4 compact models is carried out and their accuracy in performing transient analysis using SPICE is validated against well characterised mixed-mode TCAD simulation results for 35 nm devices. Static d.c. simulations are performed to test the methodology, and a useful analytic model to predict hard logic fault limitations on CMOS supply voltage scaling is derived as part of this work. Using our toolset, the effect of statistical variability introduced by random discrete dopants on the dynamic behaviour of inverters is studied in detail. As devices scaled, dynamic noise margin variation of an inverter is increased and higher output load or input slew rate improves the noise margins and its variation. Intrinsic delay variation based on CV/I delay metric is also compared using ION and IEFF definitions where the best estimate is obtained when considering ION and input transition time variations. Critical delay distribution of a path is also investigated where it is shown non-Gaussian. Finally, the impact of the cell input slew rate definition on the accuracy of the inverter cell timing characterisation in NLDM format is investigated

Parametric Yield of VLSI Systems under Variability: Analysis and Design Solutions

Variability has become one of the vital challenges that the designers of integrated circuits encounter. variability becomes increasingly important. Imperfect manufacturing process manifest itself as variations in the design parameters. These variations and those in the operating environment of VLSI circuits result in unexpected changes in the timing, power, and reliability of the circuits. With scaling transistor dimensions, process and environmental variations become significantly important in the modern VLSI design. A smaller feature size means that the physical characteristics of a device are more prone to these unaccounted-for changes. To achieve a robust design, the random and systematic fluctuations in the manufacturing process and the variations in the environmental parameters should be analyzed and the impact on the parametric yield should be addressed. This thesis studies the challenges and comprises solutions for designing robust VLSI systems in the presence of variations. Initially, to get some insight into the system design under variability, the parametric yield is examined for a small circuit. Understanding the impact of variations on the yield at the circuit level is vital to accurately estimate and optimize the yield at the system granularity. Motivated by the observations and results, found at the circuit level, statistical analyses are performed, and solutions are proposed, at the system level of abstraction, to reduce the impact of the variations and increase the parametric yield. At the circuit level, the impact of the supply and threshold voltage variations on the parametric yield is discussed. Here, a design centering methodology is proposed to maximize the parametric yield and optimize the power-performance trade-off under variations. In addition, the scaling trend in the yield loss is studied. Also, some considerations for design centering in the current and future CMOS technologies are explored. The investigation, at the circuit level, suggests that the operating temperature significantly affects the parametric yield. In addition, the yield is very sensitive to the magnitude of the variations in supply and threshold voltage. Therefore, the spatial variations in process and environmental variations make it necessary to analyze the yield at a higher granularity. Here, temperature and voltage variations are mapped across the chip to accurately estimate the yield loss at the system level. At the system level, initially the impact of process-induced temperature variations on the power grid design is analyzed. Also, an efficient verification method is provided that ensures the robustness of the power grid in the presence of variations. Then, a statistical analysis of the timing yield is conducted, by taking into account both the process and environmental variations. By considering the statistical profile of the temperature and supply voltage, the process variations are mapped to the delay variations across a die. This ensures an accurate estimation of the timing yield. In addition, a method is proposed to accurately estimate the power yield considering process-induced temperature and supply voltage variations. This helps check the robustness of the circuits early in the design process. Lastly, design solutions are presented to reduce the power consumption and increase the timing yield under the variations. In the first solution, a guideline for floorplaning optimization in the presence of temperature variations is offered. Non-uniformity in the thermal profiles of integrated circuits is an issue that impacts the parametric yield and threatens chip reliability. Therefore, the correlation between the total power consumption and the temperature variations across a chip is examined. As a result, floorplanning guidelines are proposed that uses the correlation to efficiently optimize the chip's total power and takes into account the thermal uniformity. The second design solution provides an optimization methodology for assigning the power supply pads across the chip for maximizing the timing yield. A mixed-integer nonlinear programming (MINLP) optimization problem, subject to voltage drop and current constraint, is efficiently solved to find the optimum number and location of the pads

Variability-driven module selection with joint design time optimization and post-silicon tuning

Abstract—Increasing delay and power variation are significant chal-lenges to the designers as technology scales to the deep sub-micron (DSM) regime. Traditional module selection techniques in high level synthesis use worst case delay/power information to perform the optimization, and therefore may be too pessimistic such that extra resources are used to guarantee design requirements. Parametric yield, which is defined as the probability of the synthesized hardware meeting the performance/power constraints, can be used to guide design space exploration. The para-metric yield can be effectively improved by combining both design-time variation-aware optimization and post silicon tuning techniques (such as adaptive body biasing (ABB)). In this paper, we propose a module selection algorithm that combines design-time optimization with post-silicon tuning (using ABB) to maximize design yield. A variation-aware module selection algorithm based on efficient performance and power yield gradient computation is developed. The post silicon optimization is formulated as an efficient sequential conic program to determine the optimal body bias distribution, which in turn affects design-time module selection. The experiment results show that significant yield can be achieved compared to traditional worst-case driven module selection technique. To the best of our knowledge, this is the first variability-driven high level synthesis technique that considers post-silicon tuning during design time optimization. 1 I