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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.

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.

For practical testing and detection of electromigration (EM) induced failures in dual damascene copper interconnects, one critical issue is creating stressing conditions to induce the chip to fail exclusively under EM in a very short period of time so that EM sign-off and validation can be carried out efficiently. Existing acceleration techniques, which rely on increasing temperature and current densities beyond the known limits, also accelerate other reliability effects making it very difficult, if not impossible, to test EM in isolation. In this paper, we propose novel EM wear-out acceleration techniques to address the aforementioned issue. First, we show that multisegment interconnects with reservoir and sink structures can be exploited to significantly speedup the EM wear-out process. Based on this observation, we propose three strategies to accelerate EM induced failure: 1) reservoir-enhanced acceleration; 2) sink-enhanced acceleration; and 3) a hybrid method that combines both reservoir and sink structures. We then propose several configurable interconnect structures that exploit atomic reservoirs and sinks for accelerated EM testing. Such configurable interconnect structures are very flexible and can be used to achieve significant lifetime reductions at the cost of some routing resources. Using the proposed technique, EM testing can be carried out at nominal current densities, and at a much lower temperature compared to traditional testing methods. This is the most significant contribution of this paper since, to our knowledge, this is the only method that allows EM testing to be performed in a controlled environment without the risk of invoking other reliability effects that are also accelerated by elevated temperature and current density. The simulation results show that using the proposed method, we can reduce the EM lifetime of a chip from ten years down to a few hours (about ${10^{5}} \times $ acceleration) under the 150 °C temperature limit, which is sufficient for practical EM testing of typical nanometer CMOS ICs.

Electromigration (EM) becomes a major concern for VLSI circuits as the technology advances in the nanometer regime. With Korhonen equations, EM assessment for VLSI circuits remains challenged due to the increasing integrated density. VLSI multisegment interconnect trees can be naturally viewed as graphs. Based on this observation, we propose a new graph convolution network (GCN) model, which is called EMGraph considering both node and edge embedding features, to estimate the transient EM stress of interconnect trees. Compared with recently proposed generative adversarial network (GAN) based stress image-generation method, EMGraph model can learn more transferable knowledge to predict stress distributions on new graphs without retraining via inductive learning. Trained on the large dataset, the model shows less than 1.5% averaged error compared to the ground truth results and is orders of magnitude faster than both COMSOL and state-of-the-art method. It also achieves smaller model size, $4\times$ accuracy and $14\times$ speedup over the GAN-based method.

Recently, many physics-based electromigration (EM) models have been proposed to mitigate the over-conservativeness of existing Black–Blech-based EM models. To assess EM failures, one needs to estimate how the void grows after the nucleation phase. As a result, it is important to estimate the saturation volume of a void, which is important for EM mortality check. The existing saturation void volume model only works for a single wire segment. In this paper, we propose a new model for fast estimation of the void’s saturation volume for general multisegment interconnect wires. The new model is based on the fundamental atom conservation at the steady-state condition of void growth phases. The new formula agrees with the existing saturation void volume formula for the single-segment wire case and is a natural extension of the single-segment case to general multisegment wires. In addition, we consider the impacts of the void volume on final stress distributions of the wire to further improve the accuracy of the proposed formula. Based on the new formula, we propose a new EM immortality check flow, which considers both the recently proposed EM immortality in the void nucleation phase and the void saturation volume in the growth phase. The new flow can further reduce the conservativeness of the existing EM failure effect analysis. The numerical results show that the proposed formula agrees well with a published work for two-segment cases, which are supported by experimental data. The formula is also validated by the recently proposed physics-based 3-D finite-element (FEM) analysis tool for general multisegment interconnect wires. We also demonstrate new EM immortality check flow to quickly identify the new type of immortal wires, which are nucleated but with smaller-than-critical voids.

In recent years the concern over Hardware Trojans has come to the forefront of hardware security research as these types of attacks pose a real and dangerous threat to both commercial and mission-critical systems. One interesting threat model utilizes semiconductor physics, specifically aging effects such as Electromigration (EM). However, existing methods for EM-based Trojans rely on empirical Black's models can easily lead to performance degradation and less accuracy in Trojan activation time prediction. In this article, we study the EM-based Trojan attacks based on recently developed physics-based EM models. We propose novel EM attack techniques in which the EM-induced hydrostatic stress increase in a wire is caused by wire structure or layer changes without changing the current density of the wires. The proposed techniques consist of sink/reservoir insertion or sizing and layer switching techniques based on the early and late failure modes of EM wear-out effects. As a result, the proposed techniques can have minimal impact on circuit performance, which is in contrast with existing current-density-based EM attacks. The proposed techniques can serve as a trigger for the EM attack on power/ground networks and signal and clock networks. Furthermore, we also present two potential EM attack mitigation techniques, namely, the split fabrication and burn-in testing.

This paper proposes a new power–ground (P/G) network sizing technique based on the recently proposed fast electromigration (EM) immortality check method for general multisegment interconnect wires and a new physics-based EM assessment technique for more accurate time to failure analysis. This paper first shows that the new P/G optimization problem, subject to the voltage IR drop and new EM constraints, can still be formulated as an efficient sequence of linear programing problem, where the optimization is carried out in two linear programing phases in each iteration. The new optimization will ensure that none of the wires fail if all the constraints are satisfied. However, requiring all the wires to be EM immortal can be overconstrained. To mitigate this problem, the first improvement is by means of adding reservoir branches to the mortal wires whose lifetime cannot be made immortal by wire sizing. This is a very effective approach as long as there is a sufficient reservoir area. The second improvement is to consider the aging effects of interconnect wires in the P/G networks. The idea is to allow some short-lifetime wires to fail and optimize the rest of the wires while considering the additional resistance caused by the failed wire segments. In this way, the resulting P/G networks can be optimized, such that the target lifetime of the whole P/G networks can be ensured and will become more robust and aging-aware over the expected lifetime of the chip. Numerical results on a number of IBM and self-generated power supply networks demonstrate that the new method can effectively reduce the area of the networks while ensuring immortality or enforcing target lifetime for all the wires, which is not the case for the existing current-density-constrained optimization methods.

This chapter provides techniques for power grid network sizing while considering electromigration reliability. Starting with power grid network and electromigration (EM) fundamentals. Specifically the concerns here are EM immortality and aging effects used as EM constraints when formulating the optimization problems. The chapter first shows that the new power/ground (P/G) optimization problem, subject to the voltage IR drop and new EM constraints, can still be formulated as an efficient sequence of linear programming (SLP) problem, where the optimization is carried out in two linear programming phases in each iteration. The new optimization will ensure that none of the wires fails if all the constraints are satisfied. However, requiring all the wires to be EM immortal can be over-constrained. To mitigate this problem, the improvement is to consider the aging effects of interconnect wires in P/G networks. The idea is to allow some short-lifetime wires to fail and optimize the rest of the wires while considering the additional resistance caused by the failed wire segments. In this way, the resulting P/G networks can be optimized such that the target lifetime of the whole P/G networks can be ensured and will become more robust and aging-aware over the expected lifetime of the chip.

In this work, we propose a novel learning-based task to core mapping technique to improve lifetime and reliability based on advanced deep reinforcement learning. The new method, called HAT-DRL, is based on the observation that on-chip temperature sensors may not capture the true hotspots of the chip, which can lead to suboptimal control decisions. In the new method, we first perform data-driven learning to model the hotspot activation indicator with respect to the resource utilization of different workloads. On top of this, we propose to employ deep reinforcement learning algorithm to improve the long-term reliability and minimize the performance degradation from NBTI/HCI effects. It penalizes continuously stressing the same hotspots and encourages even stressing of cores. The proposed algorithm is validated with an Intel i7-8650U four-core CPU platform executing CPU benchmarks for various hotspot activation profiles. Results show that HAT- DRL balances the stress between all cores and hotspots, and achieves 50% and 160% longer lifetime compared to non-hotspot-aware and Linux default scheduling respectively. The proposed method can also reduce the average temperature by exploiting the true-hotspot information.

In this paper, we propose a novel transient full-chip thermal map estimation method for multi-core commercial CPU based on the data-driven generative adversarial learning method. We treat the thermal modeling problem as an image-generation problem using the generative neural networks. In stead of using traditional functional unit powers as input, the new models are directly based on the measurable real-time high level chip utilizations and thermal sensor information of commercial chips without any assumption of additional physical sensors requirement. The resulting thermal map estimation method, called ThermGAN can provide tool-accurate full-chip transient thermal maps from the given performance monitor traces of commercial off-the-shelf multi-core processors. In our work, both generator and discriminator are composed of simple convolutional layers with Wasserstein distance as loss function. ThermGAN can provide the transient and real-time thermal map without using any historical data for training and inferences, which is contrast with a recent RNN-based thermal map estimation method in which historical data is needed. Experimental results show the trained model is very accurate in thermal estimation with an average RMSE of 0.47°C, namely, 0.63% of the full-scale error. Our data further show that the speed of the model is faster than 7.5ms per inference, which is two orders of magnitude faster than the traditional finite element based thermal analysis. Furthermore, the new method is ~4x more accurate than recently proposed LSTM-based thermal map estimation method and has faster inference speed. It also achieves ~2x accuracy with much less computational cost than a state-of-the-art pre-silicon based estimation method.

This paper proposes a modified particle swarm optimization (PSO) algorithm that can be used to solve a variety of fuzzy nonlinear equations, i.e. fuzzy polynomials and exponential equations. Fuzzy nonlinear equations are reduced to a number of interval nonlinear equations using alpha cuts. These equations are then sequentially solved using the proposed methodology. Finally, the membership functions of the fuzzy solutions are constructed using the interval results at each alpha cut. Unlike existing methods, the proposed algorithm does not impose any restriction on the fuzzy variables in the problem. It is designed to work for equations containing both positive and negative fuzzy sets and even for the cases when the support of the fuzzy sets extends across 0, which is a particularly problematic case.

Runtime power and thermal control is crucial in any modern processor. However, these control schemes require accurate real-time temperature information, ideally of the entire die area, in order to be effective. On-chip temperature sensors alone cannot provide the full-chip temperature information since the number of sensors that are typically available is very limited due to their high area and power overheads. Furthermore, as we will demonstrate, the peak locations within hot-spots are not stationary and are very workload dependent, making it difficult to rely on fixed temperature sensors alone. Therefore, we propose a novel approach to real-time estimation of full-chip transient heatmaps for commercial processors based on machine learning. The model derived in this work supplements the temperature data sensed from the existing on-chip sensors, allowing for the development of more robust runtime power and thermal control schemes that can take advantage of the additional thermal information that is otherwise not available. The new approach involves offline acquisition of accurate spatial and temporal heatmaps using an infrared thermal imaging setup while nominal working conditions are maintained on the chip. To build the dynamic thermal model, we apply Long-Short-Term-Memory (LSTM) neutral networks with system-level variables such as chip frequency, instruction counts, and other performance metrics as inputs. To reduce the dimensionality of the model, 2D spatial discrete cosine transformation (DCT) is first performed on the heatmaps so that they can be expressed with just their dominant DCT frequencies. Our study shows that only 6×6 DCT coefficients are required to maintain sufficient accuracy across a variety of workloads. Experimental results show that the proposed approach can estimate the full-chip heatmaps with less than $1.4^{o}$C root-mean-square-error and take only $\sim$19ms for each inference which suits well for real-time use.

Accurate thermal models suitable for system level dynamic thermal, power and reliability regulation and management are vital for many commercial multi-core processors. However, developing such accurate thermal models and identifying the related thermal-power relevant spatial locations for commercial processors is a challenging task due to the lack of information and available tools. Existing tools such as HotSpot-like thermal models may suffer from inaccuracy or inefficiency for online applications, primarily because most rely on parameters that cannot be precisely quantified, such as power-traces, while others are numerical methods not suitable for runtime use. In this work, we propose a novel approach to automatically detecting the major heat-sources on a commercial multi-core microprocessor using an infrared thermal imaging setup. Our approach involves a number of steps including 2D discrete cosine transformation filter for noise reduction on the measured thermal maps, and Laplacian transformation followed by K-mean clustering for heat-source identification. Since the identified heat-sources are the thermally vulnerable areas of the die, we propose a novel approach to deriving a thermal model capable of predicting their temperatures during runtime. We apply Long-Short-Term-Memory (LSTM) networks to build a dynamic thermal model which uses system-level variables such as chip frequency, voltage and instruction count as inputs. The model is trained and tested exclusively using measured thermal data from a commercial multi-core processor. Experimental results show that the proposed thermal model achieves very high accuracy (root-mean-square-error: 2.04°C to 2.57° C) in predicting the temperature of all the identified heat-sources on the chip.

This article presents a novel approach to real-time tracking of full-chip heatmaps for off-the-shelf microprocessors based on machine-learning. The proposed post-silicon approach, named RealMaps, only uses the existing temperature sensors and workload-independent utilization information. RealMaps does not require any knowledge of the proprietary design or manufacturing process-specific details of the chip. Consequently, the methods presented in this work can be implemented by either the original chip manufacturer or a third party alike. The approach involves offline acquisition of spatial heatmaps using a thermal imaging setup. To build the dynamic thermal model, a temporal-aware long-short-term-memory neutral network is trained with system-level features as inputs. 2D discrete cosine transformation (DCT) is performed on the heatmaps so that they can be expressed with just a few dominant DCT coefficients. This allows the model to be built to estimate just the dominant spatial features of the heatmaps, rather than the entire heatmap images, making it significantly more efficient. Experimental results from two commercial chips show that RealMaps can estimate the full-chip heatmaps with 0.9C and 1.2C root-mean-square-error respectively and take only 0.4ms for each inference. Compared to the state of the art pre-silicon approach, RealMaps shows similar accuracy, but with much less computational cost.

This paper proposes an application of type-1 and interval type-2 fuzzy logic for DC power flow analysis of electric networks with high levels of renewable energy penetration. The proposed methodology can be used for real time analysis, power system planning, and day-ahead energy markets. The uncertain variables in the DC power flow, weather-based renewable energy power generation, load and network parameters, are modelled as trapezoidal fuzzy variables using historical data from the Bonneville Power Administration's Transmission Database. DC power flow is then computed using the proposed algorithm. The method is tested on an IEEE standard 30-bus power system with a wind generator connected to the network. Line flow results computed using the proposed method show the impact of the intermittent behavior of the renewable energy generation unit on the given power system.