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Process variations and device aging impose profound challenges for circuit designers. Without a precise understanding of the impact of variations on the delay of circuit paths, guardbands, which keep timing violations at bay, cannot be correctly estimated. This problem is exacerbated for advanced technology nodes, where transistor dimensions reach atomic levels and established margins are severely constrained. Hence, traditional worst-case analysis becomes impractical, resulting in intolerable performance overheads. Contrarily, process-variation/aging-aware static timing analysis (STA) equips designers with accurate statistical delay distributions. Timing guardbands that are small, yet sufficient, can then be effectively estimated. However, such analysis is costly as it requires intensive Monte-Carlo simulations. Further, it necessitates access to confidential physics-based aging models to generate the standard-cell libraries required for STA. In this work, we employ graph neural networks (GNNs) to accurately estimate the impact of process variations and device aging on the delay of any path within a circuit. Our proposed GNN4REL framework empowers designers to perform rapid and accurate reliability estimations without accessing transistor models, standard-cell libraries, or even STA; these components are all incorporated into the GNN model via training by the foundry. Specifically, GNN4REL is trained on a FinFET technology model that is calibrated against industrial 14nm measurement data. Through our extensive experiments on EPFL and ITC-99 benchmarks, as well as RISC-V processors, we successfully estimate delay degradations of all paths -- notably within seconds -- with a mean absolute error down to 0.01 percentage points., This article will be presented in the International Conference on Compilers, Architectures, and Synthesis for Embedded Systems (CASES) 2022 and will appear as part of the ESWEEK-TCAD special issue

In this paper, electrothermal modeling and simulation of thermoelectric cooling (TEC) in the package design of VLSI systems are performed by solving coupled heat conduction and current continuity equations. We propose a new analytical solution to the coupled partial differential equations which describe temperature and voltage with the reduction from 3D to 1D. In addition to this, we derive new analytic expressions for two key performance metrics for TEC devices: the maximum temperature difference and the maximum heat-flux pumping capability, which can be guided for the optimal design of thermoelectric cooler to achieve the maximum cooling performance. Further, for the first time, we observe that when the dimensionless figure of merit ZT0 value is larger than 1, there is no maximum heat-flux value, which means the heat dissipation due to Peltier and Fourier transfer effects is larger than the heat generation caused by Joule heating effect, which can lead to more efficient TEC cooling design. The accuracy of the proposed 1D formulas is verified by 3D finite element method using COMSOL software. The compact model delivers many orders of magnitude speedup and memory saving compared to COMSOL with marginal accuracy loss. Compared with the conventional simplified 1D energy equilibrium model, the proposed analytical coupled multiphysics model is more robust and accurate.

Voltage over-scaling (VOS) optimizes energy while causing timing errors due to an unsustainable clock frequency. Many algorithms, such as in multimedia and machine learning applications, are capable of tolerating such errors. VOS has never been investigated in hardware accelerators running closed-loop algorithms. As the errors impact most decisions and actions in the subsequent steps, closed-loops dynamically change the execution flow. Timing errors should be evaluated by an accurate gate-level simulation, but a large gap still remains: how these timing errors propagate from the underlying hardware all the way up to the entire algorithm run, where they just may degrade the performance and quality of service of the application at stake? This paper tackles this issue showing a framework for VOS investigation, embracing any kind of application. Our framework simulates the VOS-induced timing errors at gate-level, dynamically linking the hardware result with the algorithm and vice versa during the evolution of the runtime of the application. The state-of-the-art VOS literature for video encoding application fails to assess the ultimate impacts of VOS-induced timing errors, as current works open the encoding loops. Unlike those, our work investigates the ultimate impact of a hardware accelerator dynamically carrying through to the video encoder all VOS-induced timing errors and preserving the full compliance to the standard. We employ a parallel sum of absolute differences (SAD) hardware accelerator as a case study. We assess the performance of the overall encoder under varying timing guardbands. Next, it is demonstrated that, under VOS, the ultimate impact in compression efficiency is related to the video’s motion intensity. Additionally, the advantages of timing guardband controlled reduction are clearly quantified in our results by virtue of the framework. Reducing at maximum 9.5% the clock frequency, energy savings (up to 16.5% in energy/operation) are achieved in SAD for video compression.

This paper introduces a methodology to use the thermal side channel as a proxy for the behavior of embedded processors to detect changes in this behavior in a cyber-physical system. Such changes may be due to software attacks, hardware attacks, and altered processors. Since control system processes are periodic computations, the thermal side channel signals exhibit a temporal pattern. This enables detection of altered code and changed device characteristics. We present a machine learning approach to estimate the activity of the embedded device from the time sequence of thermal images and show the extent that deviations from expected behavior can be detected. The approach is validated on a testbed of a multi-core processor running a periodic computational code. The infrared imager directly collects thermal imagery from the processor, which is cooled from the backside. While an external infrared imager is used in this study, it is desirable to deploy a finite number of on-chip temperature sensors. This paper shows that integrating on-chip temperature sensors allows robust real-time monitoring of the processor behavior. Finally, we also offer a machine learning approach to find the optimal locations of the on-chip sensors to aid detection.

For the first time, we demonstrate an optimization technique to synthesize circuits in the Negative Capacitance FET (NCFET) technology. NCFET is a rapidly emerging technology to replace the currently employed CMOS technology due to its profound ability to overcome the fundamental limit in scaling along with its full compatibility with the existing fabrication process. This is achieved by replacing the traditional transistor gate dielectric with a ferroelectric layer that manifests itself as a Negative Capacitance (NC), which magnifies the electric field. As a result, NCFET-based circuits can operate at a higher clock frequency without the need to increase the operating voltage. NC breaks one of the fundamental laws in physics in which the total capacitance of two capacitors connected in series becomes larger–instead of smaller in ordinary capacitors– than each of them. This could lead to sub-optimal netlists, suffering from significant increase in dynamic power and IR-drops. To suppress that, we employ the relation between delay decrease and capacitance increase of gates w.r.t ferroelectric thickness. Our technique takes an optimized netlist, obtained from commercial EDA tools, and then selectively determines the optimal ferroelectric thickness for each gate in the netlist, so that the maximum performance provided by NCFET is still achieved while the dynamic power is considerably decreased (45% on average), i.e., no trade-offs . Particularly, our technique enables the full exploitation of the performance benefits originating by NCFET, at a significantly lower (power) cost. Compared to state of the art, our technique decreases the energy-delay-product of circuits by 25% on average and reduces the deleterious effects of IR-drop by 56%. Hence, efficiency and reliability of circuits are improved without any loss in the obtained performance from NCFET.

Today's data-centric applications are incompatible with the predominant compute-centric computer architectures. The small on-chip memories of compute-centric computer architecture demand many energy-costly data transfers exposing the von-Neumann bottleneck. The Ferroelectric Field-Effect Transistor (FeFET) is an emerging Non-Volatile Memory technology enabling novel data-centric architectures that go far beyond von-Nuemann principles. FeFETs are very promising for a wide range of applications starting from on-chip memories to in-memory computing and even neuromorphic computing. Nevertheless, FeFET devices exhibit significant variations that can severely restrict their applicability. Temperature further exacerbates variation effects because it degrades ferroelectric parameters. Hence, it is indispensable to investigate and model design-time variations, run-time variations, and stochastic variations due to spatial fluctuation of ferroelectric domains under different temperatures. Dual-port FeFET has been recently proposed and demonstrated as novel structure that offers for the first time disturb-free read operation along with larger memory window (MW) compared to conventional FeFETs. However, all of the before-mentioned types of variations are amplified in such a new structure. In this work, the impact of temperature variation is analyzed for dual-port FeFETs for the first time in a cross-layer manner starting from the device level to the circuit/system levels and compared to conventional FeFET. We focus in our analysis on Hyperdimensional Computing (HDC), which is an emerging type of machine learning algorithm, that is being executed on top of FeFET-based in-memory circuits that perform efficient Hamming distance (i.e., similarity) computations. Through our cross-layer framework, we demonstrate the serious impact of variation on FeFET reliability despite the significant increase in the MW that dual-port FeFET offers. Even HDC is affected, despite its remarkable robustness against errors. All in all, our work reveals that a larger MW at the device level does not necessarily translate to benefits at the application level. Hence, investigating and modeling variability effects in a cross-layer manner is indispensable.

On-demand cooling is inevitable to maximize the processor’s performance, while fulfilling thermal constraints—this holds more in advanced technologies, where localized hotspots change during runtime. In this work, we propose to adopt an array of thin-film thermoelectric (TE) devices, which is integrated within the chip packaging, for both cooling and energy-harvesting purposes. Each TE device within the array can be during the runtime enabled either for energy harvesting or on-demand cooling. Our approach is implemented and evaluated using a mature finite elements analysis tool in which a commercial multicore mobile chip is modeled after careful calibrations together with state-of-the-art TE devices. Results demonstrate that our approach reduces the peak temperature by up to 24 $^\circ C$∘C and the average temperature by 10 $^\circ C$∘C across various benchmarks with the cost of 67.5 mW. Additionally, the harvested energy from the array of TE devices compensates for 89% of the required cooling energy.

Aging-induced degradation imposes a major challenge to the designer when estimating timing guardbands. This problem increases as traditional worst-case corners bring over-pessimism to designers, exacerbating competitive and close-to-the-edge designs. In this work, we present an accurate machine learning approach for aging-aware cell library characterization, enabling the designer to evaluate their circuit under the impact of precisely selected degradation. Unlike state of the art, we bring cell library characterization to the designer, empowering their capability in exploring the impact of aging while protecting confidential information from the foundry at the same time. Furthermore, the fast inference of cell libraries makes it feasible, for the first time, to examine aging-induced variability analysis in a Monte-Carlo fashion. Finally, we show that the designer is able to select a less pessimistic timing guardband by choosing adequate delta threshold voltage ( $\Delta {V_{th}} $ ) for their design and their needs. Our machine learning approach reaches an $R^{2}$ score of $>99\%$ for almost all data stored in the cell library. Only timing constraints show slightly less accuracy with an $R^{2}$ score around 95%. When using ML-characterized libraries in static timing analysis, we achieve errors smaller than $\pm 0.5\%$ and $\pm 0.1\%$ for path delay and dynamic power, respectively. Errors in leakage power are negligible and even smaller by orders of magnitude. Our machine learning implementation for standard cell library characterization is publicly available. Download: https://opensource.mlcad.org

In this article, we present a novel post-silicon approach to locating the dominant heat sources on commercial multicore processors using heatmaps measured via an infrared (IR) thermal imaging setup. To locate the heat sources, 2-D spatial Laplacian transformation is performed on the heatmaps followed by $K$ -means clustering to find the dominant power/heat-source clusters. This is an exclusively post-silicon approach that does not require any knowledge of the underlying design of the commercial chips other than the information that is publicly available. Since the identified clusters are the thermally vulnerable areas on the die, we then propose a machine-learning-based framework to deriving a thermal model capable of estimating their temperatures during online use. Our approach involves collecting transient temperature data of the aforementioned heat sources and synchronized high-level performance metrics from the chip, and training a long-short-term-memory (LSTM) neural network (NN) that uses the performance metrics as inputs to estimate the temperatures of the identified heat sources in real time. Since the model is meant for real-time use, we explore methods of reducing the performance overhead and inference time of the model. This includes a novel power correlation-based approach to identifying the thermally irrelevant performance metrics and eliminating them in order to reduce the input dimensionality of the model, and an analysis on network sizing to determine the ideal NN configuration for the problem at hand. The model is trained and tested exclusively using measured thermal data from commercial multicore processors. The experimental results from two Intel multicore processors (i5-3337U and i7-8650U) show that the proposed approach achieves very high accuracy (root-mean-square error: 0.55 °C–0.93 °C) in estimating the temperatures of all the identified heat sources on the chip.

In this work, we analyze the impact of self-heating effects (SHEs) on 14-nm negative capacitance (NC)-FinFET performance from device to the circuit level. The 3-D thermal TCAD simulations, after careful calibration with measurements, are performed to analyze the impact of SHE in a broad range of frequency. Furthermore, we use the TCAD calibrated BSIM-CMG model to analyze the impact of SHE in NC-FinFET at the circuit level, after including a physics-based model to capture the NC effect. For the first time, we analyze the impact of a nonuniform distribution of temperature dissipated from the channel region to gate-stack in NC-FinFETs. On account of the thermal insulating properties of the gate-stack, the ferroelectric (FE) layer is found to be cooler than the channel region under the impact of SHE. We demonstrate that neglecting that and, hence, using the channel temperature to evaluate the temperature-dependent parameter $\alpha $ (in the Landau–Khalatanikov model of NC effect) of the FE layer result in a significant overestimation of SHE-induced degradations, such as in the NC voltage gain. Based on our TCAD analysis, we propose a relation between gate-stack temperature and the channel temperature and use this to accurately model the $\alpha $ parameter and, hence, SHE in NC-FinFETs. The SHE is found to dominate for both FinFET and NC-FinFET in the gigahertz range, which eventually degrades the performance at the circuit level, which is further confirmed using ring oscillator (RO) simulations.

Approximate computing is established as a design alternative to improve the energy requirements of a vast number of applications, leveraging their intrinsic error tolerance. Voltage over-scaling (VOS) is one of the most energy-efficient approximation techniques, but its exploitation is still limited due to the large errors it induces. In this work, we investigate, for the first time, the resiliency of negative capacitance transistor (NCFET) technology to VOS in comparison to conventional CMOS technology. Our work reveals that circuits implemented using the NCFET technology exhibit much less timing errors under VOS due to the inherent voltage amplification provided by the ferroelectric layer. NCFET is one of the very promising emerging technologies that is rapidly evolving for low-power circuit as it enables the transistors to switch faster without the need to increase the voltage. We demonstrate how NCFET technology allows circuit designers to effectively employ VOS to boost the efficiency of their approximate circuits, while still keeping the induced errors marginal. Our analysis shows that the VOS-resilience of NCFET circuits enables maximizing the voltage decrease and thus, NCFET based VOS approximate circuits achieve from $1.83\times$ up to $2.78\times$ higher energy reduction compared to the corresponding FinFET circuits for the same error bounds.

Current research in the area of Neural Networks (NN) has resulted in performance advancements for a variety of complex problems. Especially, embedded system applications rely more and more on the utilization of convolutional NNs to provide services such as image/audio classification and object detection. The core arithmetic computation performed during NN inference is the multiply-accumulate (MAC) operation. In order to meet tighter and tighter throughput constraints, NN accelerators integrate thousands of MAC units resulting in a significant increase in power consumption. Approximate computing is established as a design alternative to improve the efficiency of computing systems by trading computational accuracy for high energy savings. In this work, we bring approximate computing principles and NN inference together by designing NN specific approximate multipliers that feature multiple accuracy levels at run-time. We propose a time-efficient automated framework for mapping the NN weights to the accuracy levels of the approximate reconfigurable accelerator. The proposed weight-oriented approximation mapping is able to satisfy tight accuracy loss thresholds, while significantly reducing energy consumption without any need for intensive NN retraining. Our approach is evaluated against several NNs demonstrating that it delivers high energy savings (17.8% on average) with a minimal loss in inference accuracy (0.5%).

Neural processing units (NPUs) are becoming an integral part in all modern computing systems due to their substantial role in accelerating neural networks (NNs). The significant improvements in cost-energy-performance stem from the massive array of multiply accumulate (MAC) units that remarkably boosts the throughput of NN inference. In this work, we are the first to investigate the thermal challenges that NPUs bring, revealing how MAC arrays, which form the heart of any NPU, impose serious thermal bottlenecks to on-chip systems due to their excessive power densities. For the first time, we explore: 1) the effectiveness of precision scaling and frequency scaling (FS) in temperature reductions and 2) how advanced on-chip cooling using superlattice thin-film thermoelectric (TE) open doors for new tradeoffs between temperature, throughput, cooling cost, and inference accuracy in NPU chips. Our work unveils that hybrid thermal management , which composes different means to reduce the NPU temperature, is a key. To achieve that, we propose and implement PFS-TE technique that couples precision and FS together with superlattice TE cooling for effective NPU thermal management. Using commercial signoff tools, we obtain accurate power and timing analysis of MAC arrays after a full-chip design is performed based on 14-nm Intel FinFET technology. Then, multiphysics simulations using finite-element methods are carried out for accurate heat simulations in the presence and absence of on-chip cooling. Afterward, comprehensive design-space exploration is presented to demonstrate the Pareto frontier and the existing tradeoffs between temperature reductions, power overheads due to cooling, throughput, and inference accuracy. Using a wide range of NNs trained for image classification, experimental results demonstrate that our novel NPU thermal management increases the inference efficiency (TOPS/Joule) by $1.33\times $ , $1.87\times $ , and $2\times $ under different temperature constraints; 105 °C, 85 °C, and 70 °C, respectively, while the average accuracy drops merely from 89.0% to 85.5%.

We demonstrate a novel technique that employs transistor short-term aging effects in integrated circuits (ICs) to detect hardware Trojans in embedded systems. In advanced technology nodes (≤ 45 nm), voltage scaling in combination with short-term aging opens doors for short-term degradations. The induced short-term degradations result in dynamic variation of delays along various paths within the IC. Aging degradation generated under fast voltage switching from high to low results in bit errors at the circuit output. Our experiments use short-term aging-aware standard cell libraries to show the effectiveness of short-term aging to detect hardware Trojans. We extract a rich set of features that capture bit error patterns at the outputs of the IC. We use a one class SVM-based classifier that uses these features to learn the distribution of bit errors at the outputs of a clean IC. We discern the deviation in the pattern of bit errors due to a Trojan in the IC from the baseline distribution. To reiterate, the method uses the model of a clean IC. Furthermore, it is robust against chip-to-chip variations. We illustrate the technique on six Trojans from Trust-Hub spanning two cryptographic chips and an embedded PIC microcontroller. Our approach detects Trojans with an accuracy ≥ 95%. It is easier to detect Trojans in an optimized-netlist circuit as more paths are close to the critical path. Even when the circuit is not optimized (i.e., when very few paths are close to the critical path), short-term aging plus mild overclocking can detect Trojans with high accuracy.

Power and energy consumption are the key optimization goals in all modern processors. Negative capacitance field-effect transistors (NCFETs) are a leading emerging technology that promises outstanding performance in addition to better energy efficiency. The thickness of the added ferroelectric layer as well as frequency and voltage are the key parameters that impact the power and energy of NCFET-based processors in addition to the characteristics of runtime workloads. Unlike existing CMOS technologies, operating NCFET-based processors at a higher frequency than the required minimum can result in power/energy minimization. The optimal operating point, however, strongly depends on dynamic workload characteristics and technology parameters. In this work, we propose and implement the first NCFET-aware power and energy management approach that minimizes the processor’s power and energy through optimal voltage/frequency selection under different runtime scenarios. Such an NCFET-aware approach does not result in any tradeoff between power/energy and performance. Instead, it can achieve higher performance while minimizing energy. A comprehensive, simulation-based evaluation of our runtime management under realistic workloads demonstrates up to 58% energy saving with $2.1\times $ higher performance, and 46% power saving compared to conventional NCFET-unaware management techniques, over the total execution of a benchmark. Compared to state-of-the-art NCFET-aware management techniques, our technique provides up to 49% energy saving and 32% power saving.

A cross-layer design space exploration (DSE) method based on a proposed co-simulation technique is presented herein. The proposed method is demonstrated evaluating the impacts on both coding efficiency and power dissipation of applying distinct approximate logic operators in a s $\mu {\mathrm{ m}}$ of absolute differences (SAD) kernel that accelerates an H.265/HEVC (high-efficiency video coding) encoder. The proposed method simulates the gate-level circuit dynamically inside the application, with realistic results of the impact of the adder-tree approximate logic implementation on both quality and encoder bit-rate results. A comprehensive DSE is shown herein, with 13 types of 6 classes of approximate adders in the SAD accelerator hardware blocks. Over 3,000 logic variants of approximations at gate-level were developed. Actual video sequences as inputs to the x265 software encoder are co-simulated, to dynamically capture the video motion-estimation (ME) behavior in the presence of logic approximations. While the prior art that only estimates the impact of the approximate logic on power, area, and quality on static designs with statistical assumptions, which are agnostic to the actual algorithm data-dependent behavior in the application, our method explores accurately the trade-off between power dissipation and coding efficiency dynamically over the entire HEVC encoding. Our approach shows that the lower-part-or and error-tolerant adder I approximate adders, as well as truncation-to-zero deliver better compression-power trade-offs, with substantial differences from the static analysis.

Self-heating effect (SHE) is a major reliability concern in current and upcoming technology nodes due to its ability to increase the channel’s temperature of transistor and leading to degradations in the key electrical characteristics such as carrier mobility. In this brief, we study SHE in a full processor at the 7nm FinFET technology node. This is the first work to analyze the impact that executed workloads on top of processors have on stimulating SHE. As matter of fact, SHE in transistors is driven by the workload-induced switching activities. When it comes to evaluating SHE, state of the art typically assumes that the switching frequency $f_{sw}$ and operating clock frequency $f_{clk}$ of a circuit are the same, concluding that SHE is not a concern in digital circuits that operate in the GHz-range like processors. After analysis a wide range of workloads, our investigation revealed that the majority of transistors in the processor’s netlist exhibit a switching frequency in the kHz-range even though the processor’s clock is in the GHz-range. This is because that the majority of transistors are within the data paths and hence their switching is driven by the workload data and not by the clock itself. In addition, we also demonstrate for the first time the important role that the duty cycle (on-/off-ratio) induced by the running workload has on modeling SHE. All in all, the relatively low switching activities together with skewed duty cycles induce a wide variety in channel temperatures. Thus, highlighting the importance of considering the workload when studying SHE.

Due to the increasing size of s (s), their design and optimization phases (i.e., ) grow increasingly complex. At design time, a large design space needs to be explored to find an implementation that fulfills all specifications and then optimizes metrics like energy, area, delay, reliability, etc. At run time, a large configuration space needs to be searched to find the best set of parameters (e.g., voltage/frequency) to further optimize the system. Both spaces are infeasible for exhaustive search typically leading to heuristic optimization algorithms that find some trade-off between design quality and computational overhead. ML can build powerful models that have successfully been employed in related domains. In this survey, we categorize how () may be used and is used for design-time and run-time optimization and exploration strategies of s. A meta-study of published techniques unveils areas in that are well-explored and underexplored with, as well as trends in the employed algorithms. We present a comprehensive categorization and summary of the state of the art on for. Finally, we summarize remaining challenges and promising open research directions.

In this work, we investigate the impact of Si-SiO2 interface traps on the performance of negative capacitance transistor, which is a promising emerging technology that aims at achieving a steep sub-threshold slope. Interface traps induced degradation is well known to be one of the major concerns when it comes to reliability. We focus on investigating the impact of different interface trap concentrations on the figures of merit of both the devices and circuits. Our investigation is performed using TCAD models, which are well calibrated against 14nm production quality FinFETs. This allows accurate analysis and modeling of the impact of NC on the electric field across the SiO2 layer. Then, the industry compact model of FinFET (BSIM-CMG) is fully calibrated to reproduce TCAD data. In addition, a physics-based NC model is integrated and solved self-consistently within the BSIM-CMG model in which TCAD data of NC-pFinFETs and NC-nFinFETs are also well matched. This allows studying how interface traps induced degradation can impact circuits. Our results demonstrate that the amplified electric field across the SiO2 layer within NC-pFinFET due to NC effect leads to a higher interface trap concentration. This, in turn, results in a larger degradation in the NC-pFinFET compared to its pFinFET counterpart - when both of these devices are operated at the same nominal supply voltage of the 14nm node. However, at the same interface trap concentration, the NC-pFinFET always exhibits less degradation than the baseline pFinFET due to the former's better electrostatic integrity on account of voltage amplification effect. With respect to circuits, we study both Ring Oscillator (RO) and 6-T SRAM cell circuits. We show how the frequency of RO in the case of NC-FinFET is always less impacted by interface trap induced degradations compared to its counterpart FinFET-based RO. For 6-T SRAM cell, we demonstrate how the key reliability metrics such as hold noise margin, read noise margin, and write noise margin are also less impacted by the induced degradations in NC-FinFET SRAMs compared to the baseline FinFET SRAMs. This is because of the much better electrostatic integrity that NC provides.

Bias temperature instability (BTI) is a major reliability concern in the current and upcoming technologies. Current mitigation techniques mainly target circuit and system levels through reducing the stimuli that govern BTI. Such mitigation techniques typically come with non-negligible overheads, which might not be acceptable—especially in smaller technology nodes where the available design margins are tighter. Semiconductor vendors report different BTI degradations in their technology and hence circuit designers need to consider such technology features when designing for reliability. Recently, TSMC reported that BTI strongly depends on the gate length ( ${L}$ ) of transistors in their 10nm technology node. In this brief, we are the first to investigate the role that gate length dependence of BTI may play as a new degree of freedom in the design for reliability. Based on the reported data from TSMC, we propose to incorporate the gate length dependency into a state-of-the-art physics-based BTI model. Then, we propose a new method of transistor stacking that optimizes circuits (e.g., SRAM cells) with respect to on/off current ratios in which ${L}$ -dependency of BTI is taken into account. Compared to state of the art, our approach results in BTI-hardened SRAM cells with 50% better on/off current ratio along with $3\times$ better reliability at the cost of a 20% area overhead.

Variability is one of the major challenges for CMOS in the nano era. Manufacturers test each circuit sample to ensure that samples that do not meet the desired specification are discarded. However, testing is only effective for variability, which is observable right after manufacturing, such as geometric variations, work function, and random dopant fluctuation. This is in contrast to time-dependent variability (TDV), i.e., differences in the defects of transistors, which is not macroscopically observable immediately after manufacturing. In fact, defects are electrically neutral until they capture a carrier [with mechanisms called bias temperature instability (BTI) and random telegraph noise (RTN)] and thus become observable through their induced degradation. Therefore, transistors which are characterized identically after manufacturing will drift apart during their lifetime, as their susceptibility to effects such as BTI and RTN is different. In this paper, we model for the first time TDV from a defect-centric physical perspective all the way to the circuit level. Our novel defect-centric transistor reliability specification provides a fast, yet accurate method to estimate an upper bound for TDV on the transistor level, while our novel worst cell (WCL) and worst value (WVL) libraries allow for fast evaluation of the impact of TDV on the timing of circuits. Our approach is fully compatible with existing EDA tool flows, allowing us to model and optimize complex circuits like full microprocessors. By evaluating the impact of TDV with our reliability specification and variability-aware cell libraries, we are able to model TDV, which allowed us to reduce the required defect variability guardband by 46%. In addition, we provide design optimization strategies on each abstraction level such as limiting continuous stress, transistor hardening, and implement a novel variability-aware synthesis to achieve up to 57% additional guardband reduction.

The design of reliable circuits in current semiconductor technologies requires worst-case estimations of degradation effects during chip signoff. Hence, semiconductor vendors provide worst-case cell delays in the form of slow/slow process corners and best-case cell delays in fast/fast process corners. By providing these corner cases, EDA signoff tools can accurately estimate the circuit timing in which a reliable operation (i.e., no timing violations) is guaranteed for the projected lifetime. State of the art assumes that a standard cell exhibits the worst-case delay increase when all of its transistors uniformly exhibit worst-case aging-induced degradation. As our first contribution, we are the first to demonstrate that this assumption is incorrect and leads to a considerable underestimation of up to 55% in circuit timing. To find the worst-case cell delay, instead of searching across all combinations of non-uniform transistor degradations, we propose reducing the search space by exploiting circuit topology, that is, using cell input vectors to determine transistor duty cycles. Our aim is to find the worst-case input vectors of a cell, which lead to the highest possible shift in rise and fall propagation delay for each standard cell. Since the number of inputs of a standard cell is significantly smaller than its number of transistors, exploring this reduced search space becomes feasible. We show how considering a uniform worst-case degradation for each transistor underestimates the actual degradation in standard cells. In fact, actual non-uniform worst-case inputs vectors result in 83% higher standard cell delay on average (compared to applying peak degradation uniformly) with a peak of $60\boldsymbol \times $ for an inverter under a high load capacitance.

In this paper, we present a comprehensive analysis of the impact of aging on the interconnection network of field-programmable gate arrays (FPGAs) and propose novel approaches to mitigate the aging effects on the routing network. We first show the insignificant impact of aging on data integrity of FPGAs, i.e., static noise margin and soft error rate of the configuration cells, as well as we show the negligible impact of the mentioned degradations on the FPGA performance. As such, we focus on the performance degradation of datapath transistors. In this regard, we propose a routing accompanied by a placement algorithm that prevents constant stress on transistors by evenly distributing the stress through the interconnection resources. By observing the impact of the signal probability on the aging of routing buffers, we enhance the synthesis flow as well as augment the proposed routing algorithm to converge the signal probabilities toward aging-friendly values. Experimental results over a set of industrial benchmarks and commerciallike FPGA architecture indicate the effectiveness of the proposed method with 64.3% reduction of stress duration in multiplexers and up to 45.2% improvement of the degradation of buffers. Altogether, the proposed method reduces the timing guardband by from 14.1% to 31.7%, depending on the FPGA routing architecture.

Transistor aging phenomena manifest themselves as degradations in the main electrical characteristics of transistors. Over time, they result in a significant increase of cell propagation delay, leading to errors due to timing violations, since the operating frequency becomes unsustainable as the circuit ages. Conventional techniques employ timing guardbands to mitigate aging-induced delay increase, which leads to considerable performance losses from the beginning of the circuit’s lifetime. Leveraging the inherent error resilience of a vast number of application domains, approximate computing was recently introduced as an aging mitigation mechanism. In this work, we present the first automated framework for generating aging-aware approximate circuits . Our framework, by applying directed gate-level netlist approximation, induces a small functional error and recovers the delay degradation due to aging. As a result, our optimized circuits eliminate aging-induced timing errors. Experimental evaluation over a variety of arithmetic circuits and image processing benchmarks demonstrates that for an average error of merely $5\times 10^{-3}$ , our framework completely eliminates aging-induced timing guardbands. Compared to the respective baseline circuits without timing guardbands (i.e., iso-performance evaluation), the error of the circuits generated by our framework is $1208\times $ smaller.

A framework is proposed for activity-dependent timing degradation due to p-FET negative bias temperature instability (NBTI) in digital circuits. A fixed-time compact model is proposed for NBTI and validated with physical model predictions for various digital circuits under different input signal slew and fan-out load conditions. The model is used to predict the timing degradation in digital circuits under arbitrary input activities. An equivalent degradation level is found that can be applied to all p-FETs in the circuit and can serve as an upper bound of degradation due to arbitrary input activity and avoid the conservative worst case dc analysis. The activity dependence is studied in microprocessors as well as arithmetic circuits under different actual workloads.

A comprehensive simulation flow is demonstrated to assess the negative-bias temperature instability (NBTI) impact on the performance and power of digital logic circuits based on the 14-nm node FinFET technology. Fully calibrated technology computer-aided design simulations are used to determine the preaged and postaged device characteristics; the results are used for calibrating the BSIM-CMG compact model. Standard cell libraries are characterized next, by only threshold voltage shift ( $\Delta {V}_{\text {T}}$ ) and by both $\Delta {V}_{\text {T}}$ and subthreshold slope shift ( $\Delta $ SS). Various benchmark circuits are synthesized and analyzed, and their timing degradation is compared to ring oscillator results. The consequence of ignoring $\Delta $ SS on OFF current and static power ( ${P}_{\text {static}}$ ) is estimated.

In this article, we will present recent advances in reliability effects such as electromigration on interconnects and Negative/Positive Bias Temperature Instability (N/P BTI) effects on CMOS devices, which are the most important reliability concerns for VLSI systems specifically at the nanometer regime. We will start with the grand reliability challenges facing the semi-conductor and computing industry. Then, we will first present recent advances in the electromigration (EM) modeling and assessment techniques at the circuit level, the full-chip level and the system level. We will focus on the recently proposed advanced EM modeling techniques including stress-oriented physic-based EM models, EM modeling considering the time-varying temperature and current density changes, EM recovery effect modeling, the more general physics-based 3-phase EM models and the finite-difference-method based numerical analysis technique for dynamic EM stress analysis. Then we will present recent developments for dynamic reliability management at the system level, where EM-induced lifetime and performance can be traded off and the EM recovery effects can be leveraged for a longer lifetime on different computing platforms. For BTI effects, we will briefly explain the key mechanisms behind it first. Then, we will demonstrate how to bring aging-awareness to EDA tool flows based on our so-called degradation-aware cell libraries. Afterwards, we will present the impact of BTI effects on the leakage and dynamic power showing that BTI impact not only affects circuits’ delay over time (as in the traditional view), but also the overall power of circuits. Towards removing guard-bands and hence increase the efficiency, we will present how aging-induced stochastic timing errors can be translated into deterministic and controlled approximations in which aging effects are suppressed with a minimum loss in quality. Finally, we will demonstrate short-term aging effect which is a recent discovery that is hardly explored until now. In fact, short-term aging effects are a paradigm shift in BTI from sole long-term reliability degradation, which is observable in the order of months and years as in the traditional view, to an emerging reliability degradation, which is observable in a significantly smaller time domain in the order of milliseconds and even microseconds. Some of the developed EM models and assessment programs can be downloaded at https://github.com/sheldonucr/physics_based_em_assessment_analysis . The developed aging models, degradation-aware cell libraries, reliability framework, etc. are publicly available at: http://ces.itec.kit.edu/dependable-hardware.php . They are ready to be directly used with existing EDA tool flows like Synopsys without requiring any modifications.

Near-threshold computing (NTC) poses stringent constraints on designing reliable circuits, as degradations have a magnified impact at lower supply voltages ( $V_{\text {dd}}$ ) compared with super-threshold supply voltages. While phenomena, such as bias temperature instability (BTI) scale down with $V_{\text {dd}}$ , mitigate their magnified impact with reduced degradations and, thus, have little impact on NTC reliability. Process variation (PV) and random telegraph noise (RTN) do not scale with $V_{\text {dd}}$ and, therefore, become key reliability challenges in NTC. On the other hand, in super-threshold computing (STC), PV and BTI are the dominant phenomena, as BTI induces considerable degradations at nominal $V_{\text {dd}}$ and PV imposes large enough shifts to matter at any supply voltage. Therefore, to allow $V_{\text {dd}}$ -scaling from super-to near-threshold, we need to consider all of BTI, RTN, and PV. Ergo, we present a unified RTN and BTI model that models their shared physical origin and is validated against experimental data across a wide voltage range. Our unified model and PV model capture the joint impact of RTN, BTI, and PV within a probabilistic reliability estimation for NTC and STC circuits. We employed our proposed model to analyze the reliability of SRAM cells showing how taking error correction codes into account is able to mitigate the deleterious effects of BTI, RTN, and PV by 36% compared with unprotected circuits.

Editor’s note: Process variations, aging and wearout, are nonidealities that lead to suboptimal system performance and increased power. In order to understand the effects of these degradation effects, until now, researchers have investigated them thoroughly, but separately from each other. What this article shows is that process variations and wearout are not independent from each other and they need to be considered together. The article shows designers how they can sometimes take advantage of these interdependencies to safely reduce design margins, while in other cases, it is possible that the interdependencies conspire to amplify the effect of the degradation effects in catastrophic ways. —Mircea Stan, University of Virginia

Side-channel attacks have empowered bypassing of cryptographic components in circuits. Power side-channel (PSC) attacks have received particular traction, owing to their noninvasiveness and proven effectiveness. Aside from prior art focused on conventional technologies, this is the first work to investigate the emerging negative capacitance transistor (NCFET) technology in the context of PSC attacks. We implement a CAD flow for the PSC evaluation at design time. It leverages industry-standard design tools, while also employing the widely accepted correlation power analysis (CPA) attack. Using standard-cell libraries based on the 7-nm FinFET technology for NCFET and its counterpart CMOS setup, our evaluation reveals that NCFET-based circuits are more resilient to the classical CPA attack, due to the considerable effect of negative capacitance on the switching power. We also demonstrate that the thicker the ferroelectric layer, the higher the resiliency of the NCFET-based circuit, which opens new doors for optimization and tradeoffs.

Negative capacitance field-effect transistor (NCFET) pushes the subthreshold swing beyond its fundamental limit of 60 mV/decade by incorporating a ferroelectric material within the gate-stack of transistor. Such a material manifests itself as an NC that provides an internal voltage amplification for the transistor resulting in higher ON-current levels. Hence, the performance of processors can be boosted while the operating voltage still remains the same. However, having an NC makes the total gate terminal capacitance larger. Although the impact of that on compensating the gained performance has already been studied in the literature, this paper is the first to explore the impact of NC on exacerbating the IR-drop problem in processors. In fact, voltage fluctuation in the power delivery network (PDN) due to IR-drops is one of the prominent sources of performance loss in processors, which necessitates adding timing guardbands to sustain a reliable operation during runtime. In this paper, we study NC-FinFET standard cells and processor for the 7-nm technology node. We demonstrate that NC, on the one hand, results in larger IR-drops due to the increase in current densities across the chip, which leads to a higher stress on the PDN. However, the internal voltage amplification provided by NC, on the other hand, compensates to some degree the voltage reduction caused by IR-drop. We investigate, from physics all the way to full-chip (GDSII) level, how the overall performance of a processor is affected under the impact that NC has on magnifying and compensating IR-drop.

With technology scaling, the susceptibility of circuits to different reliability degradations is steadily increasing. Aging in transistors due to bias temperature instability (BTI) and voltage fluctuation in the power delivery network of circuits due to IR-drops are the most prominent. In this paper, we are reporting for the first time that there are interdependences between voltage fluctuation and BTI aging that are nonnegligible. Modeling and investigating the joint impact of voltage fluctuation and BTI aging on the delay of circuits, while remaining compatible with the existing standard design flow, is indispensable in order to answer the vital question, “what is an efficient (i.e., small, yet sufficient) timing guardband to sustain the reliability of circuit for the projected lifetime?” This is, concisely, the key goal of this paper. Achieving that would not be possible without employing a physics-based BTI model that precisely describes the underlying generation and recovery mechanisms of defects under arbitrary stress waveforms. For this purpose, our model is validated against varied semiconductor measurements covering a wide range of voltage, temperature, frequency, and duty cycle conditions. To bring reliability awareness to existing EDA tool flows, we create standard cell libraries that contain the delay information of cells under the joint impact of aging and IR-drop. Our libraries can be directly deployed within the standard design flow because they are compatible with existing commercial tools (e.g., Synopsys and Cadence). Hence, designers can leverage the mature algorithms of these tools to accurately estimate the required timing guardbands for any circuit despite its complexity. Our investigation demonstrates that considering aging and IR-drop effects independently , as done in the state of the art, leads to employing insufficient and thus unreliable guardbands because of the nonnegligible (on average 15% and up to 25%) underestimations. Importantly, considering interdependences between aging and IR-drop does not only allow correct guardband estimations, but it also results in employing more efficient guardbands.