Multiphysics modeling and simulation for large-scale integrated circuits

This dissertation is a process of seeking solutions to two important and challenging problems related to the design of modern integrated circuits (ICs): the ever increasing couplings among the multiphysics and the large problem size arising from the escalating complexity of the designs. A multiphysics-based computer-aided design methodology is proposed and realized to address multiple aspects of a design simultaneously, which include electromagnetics, heat transfer, fluid dynamics, and structure mechanics. The multiphysics simulation is based on the finite element method for its unmatched capabilities in handling complicate geometries and material properties. The capability of the multiphysics simulation is demonstrated through its applications in a variety of important problems, including the static and dynamic IR-drop analyses of power distribution networks, the thermal-ware high-frequency characterization of through-silicon-via structures, the full-wave electromagnetic analysis of high-power RF/microwave circuits, the modeling and analysis of three-dimensional ICs with integrated microchannel cooling, the characterization of micro- and nanoscale electrical-mechanical systems, and the modeling of decoupling capacitor derating in the power integrity simulations. To perform the large-scale analysis in a highly efficient manner, a domain decomposition scheme, parallel computing, and an adaptive time-stepping scheme are incorporated into the proposed multiphysics simulation. Significant reduction in computation time is achieved through the two numerical schemes and the parallel computing with multiple processors

Effective thermal conductivity of wire-woven bulk Kagome sandwich panels

AbstractThermal transport in a highly porous metallic wire-woven bulk Kagome (WBK) is numerically and analytically modeled. Based on topology similarity and upon introducing an elongation parameter in thermal tortuosity, an idealized Kagome with non-twisted struts is employed. Special focus is placed upon quantifying the effect of topological anisotropy of WBK upon its effective conductivity. It is demonstrated that the effective conductivity reduces linearly as the porosity increases, and the extent of the reduction is significantly dependent on the orientation of WBK. The governing physical mechanism of anisotropic thermal transport in WBK is found to be the anisotropic thermal tortuosity caused by the intrinsic anisotropic topology of WBK

Large-Scale Discrete Fourier Transform on TPUs

In this work, we present two parallel algorithms for the large-scale discrete Fourier transform (DFT) on Tensor Processing Unit (TPU) clusters. The two parallel algorithms are associated with two formulations of DFT: one is based on the Kronecker product, to be specific, dense matrix multiplications between the input data and the Vandermonde matrix, denoted as KDFT in this work; the other is based on the famous Cooley-Tukey algorithm and phase adjustment, denoted as FFT in this work. Both KDFT and FFT formulations take full advantage of TPU's strength in matrix multiplications. The KDFT formulation allows direct use of nonuniform inputs without additional step. In the two parallel algorithms, the same strategy of data decomposition is applied to the input data. Through the data decomposition, the dense matrix multiplications in KDFT and FFT are kept local within TPU cores, which can be performed completely in parallel. The communication among TPU cores is achieved through the one-shuffle scheme in both parallel algorithms, with which sending and receiving data takes place simultaneously between two neighboring cores and along the same direction on the interconnect network. The one-shuffle scheme is designed for the interconnect topology of TPU clusters, minimizing the time required by the communication among TPU cores. Both KDFT and FFT are implemented in TensorFlow. The three-dimensional complex DFT is performed on an example of dimension $8192 \times 8192 \times 8192$ with a full TPU Pod: the run time of KDFT is 12.66 seconds and that of FFT is 8.3 seconds. Scaling analysis is provided to demonstrate the high parallel efficiency of the two DFT implementations on TPUs

Development of a micro-indentation device for measuring the mechanical properties of soft materials

AbstractIndentation is a simple and nondestructive method to measure the mechanical properties of soft materials, such as hydrogels, elastomers and soft tissues. In this work, we have developed a micro-indentation system with high-precision to measure the mechanical properties of soft materials, where the shear modulus and Poisson's ratio of the materials can be obtained by analyzing the load–relaxation curve. We have validated the accuracy and stability of the system by comparing the measured mechanical properties of a polyethylene glycol sample with that obtained from a commercial instrument. The mechanical properties of another typical polydimethylsiloxane sample submerged in heptane are measured by using conical and spherical indenters, respectively. The measured values of shear modulus and Poisson's ratio are within a reasonable range

Bioprinting-Based High-Throughput Fabrication of Three-Dimensional MCF-7 Human Breast Cancer Cellular Spheroids

ABSTRACTCellular spheroids serving as three-dimensional (3D) in vitro tissue models have attracted increasing interest for pathological study and drug-screening applications. Various methods, including microwells in particular, have been developed for engineering cellular spheroids. However, these methods usually suffer from either destructive molding operations or cell loss and non-uniform cell distribution among the wells due to two-step molding and cell seeding. We have developed a facile method that utilizes cell-embedded hydrogel arrays as templates for concave well fabrication and in situ MCF-7 cellular spheroid formation on a chip. A custom-built bioprinting system was applied for the fabrication of sacrificial gelatin arrays and sequentially concave wells in a high-throughput, flexible, and controlled manner. The ability to achieve in situ cell seeding for cellular spheroid construction was demonstrated with the advantage of uniform cell seeding and the potential for programmed fabrication of tissue models on chips. The developed method holds great potential for applications in tissue engineering, regenerative medicine, and drug screening

Enable Language Models to Implicitly Learn Self-Improvement From Data

Large Language Models (LLMs) have demonstrated remarkable capabilities in open-ended text generation tasks. However, the inherent open-ended nature of these tasks implies that there is always room for improvement in the quality of model responses. To address this challenge, various approaches have been proposed to enhance the performance of LLMs. There has been a growing focus on enabling LLMs to self-improve their response quality, thereby reducing the reliance on extensive human annotation efforts for collecting diverse and high-quality training data. Recently, prompting-based methods have been widely explored among self-improvement methods owing to their effectiveness, efficiency, and convenience. However, those methods usually require explicitly and thoroughly written rubrics as inputs to LLMs. It is expensive and challenging to manually derive and provide all necessary rubrics with a real-world complex goal for improvement (e.g., being more helpful and less harmful). To this end, we propose an ImPlicit Self-ImprovemenT (PIT) framework that implicitly learns the improvement goal from human preference data. PIT only requires preference data that are used to train reward models without extra human efforts. Specifically, we reformulate the training objective of reinforcement learning from human feedback (RLHF) -- instead of maximizing response quality for a given input, we maximize the quality gap of the response conditioned on a reference response. In this way, PIT is implicitly trained with the improvement goal of better aligning with human preferences. Experiments on two real-world datasets and one synthetic dataset show that our method significantly outperforms prompting-based methods.Comment: 28 pages, 5 figures, 4 table

Instruction-Following Evaluation for Large Language Models

One core capability of Large Language Models (LLMs) is to follow natural language instructions. However, the evaluation of such abilities is not standardized: Human evaluations are expensive, slow, and not objectively reproducible, while LLM-based auto-evaluation is potentially biased or limited by the ability of the evaluator LLM. To overcome these issues, we introduce Instruction-Following Eval (IFEval) for large language models. IFEval is a straightforward and easy-to-reproduce evaluation benchmark. It focuses on a set of "verifiable instructions" such as "write in more than 400 words" and "mention the keyword of AI at least 3 times". We identified 25 types of those verifiable instructions and constructed around 500 prompts, with each prompt containing one or more verifiable instructions. We show evaluation results of two widely available LLMs on the market. Our code and data can be found at https://github.com/google-research/google-research/tree/master/instruction_following_eva