Thermal interface materials: From fundamental research to applications

SusMat, Journal Year: 2024, Volume and Issue: unknown

Published: Sept. 23, 2024

Abstract The miniaturization, integration, and high data throughput of electronic chips present challenging demands on thermal management, especially concerning heat dissipation at interfaces, which is a fundamental scientific question as well an engineering problem—a death problem called in semiconductor industry. A comprehensive examination interfacial resistance has been given from physics perspective 2022 Review Modern Physics . Here, we provide detailed overview materials perspective, focusing the optimization structure compositions interface (TIMs) interact/contact with source sink. First, discuss impact conductivity, bond line thickness, contact TIMs. Second, it pointed out that there are two major routes to improve transfer through interface. One reduce TIM's ( R TIM ) TIMs strategies like incorporating conductive fillers, enhancing treatment techniques. other c by improving effective contact, strengthening bonding, utilizing mass gradient alleviate vibrational mismatch between source/sink. Finally, such challenges theories, potential developments sustainable TIMs, application AI design also explored.

Language: Английский

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Topological thermal transport

Nature Reviews Physics, Journal Year: 2024, Volume and Issue: 6(9), P. 554 - 565

Published: Aug. 15, 2024

Language: Английский

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Free-form and multi-physical metamaterials with forward conformality-assisted tracing

Nature Computational Science, Journal Year: 2024, Volume and Issue: 4(7), P. 532 - 541

Published: July 9, 2024

Language: Английский

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Machine learning aided understanding and manipulating thermal transport in amorphous networks

Journal of Applied Physics, Journal Year: 2024, Volume and Issue: 135(19)

Published: May 16, 2024

Thermal transport plays a pivotal role across diverse disciplines, yet the intricate relationship between amorphous network structures and thermal conductance properties remains elusive due to absence of reliable comprehensive network’s dataset be investigated. In this study, we have created comprising multiple varying sizes, generated through combination node disturbance method Delaunay triangulation, fine-tune an initially random toward both increased decreased C. The tuning process is guided by simulated annealing algorithm. Our findings unveil that C inversely dependent on normalized average shortest distance Lnorm connecting heat source nodes sink nodes, which determined topological structure. Intuitively, with associated number bonds oriented along direction, shortens transfer from node. Conversely, encounters impedance augmented perpendicular demonstrated Lnorm. This can described power law C=Lnormα, applicable diverse-sized networks

Language: Английский

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A cyclical route linking fundamental mechanism and AI algorithm: An example from tuning Poisson's ratio in amorphous networks

Applied Physics Reviews, Journal Year: 2024, Volume and Issue: 11(3)

Published: July 2, 2024

“AI for science” is widely recognized as a future trend in the development of scientific research. Currently, although machine learning algorithms have played crucial role research with numerous successful cases, relatively few instances exist where AI assists researchers uncovering underlying physical mechanisms behind certain phenomenon and subsequently using that mechanism to improve algorithms' efficiency. This article uses investigation into relationship between extreme Poisson's ratio values structure amorphous networks case study illustrate how methods can assist revealing mechanisms. Upon recognizing relies on low-frequency vibrational modes dynamical matrix, we then employ convolutional neural network, trained matrix instead traditional image recognition, predict much higher Through this example, aim showcase artificial intelligence play fundamental mechanisms, which improves significantly.

Language: Английский

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Inverse Design of AlGaN/GaN HEMT RF Device with Source Connected Field Plate

Advanced Theory and Simulations, Journal Year: 2025, Volume and Issue: unknown

Published: Jan. 6, 2025

Abstract This study introduces a novel approach in the prediction, design, and optimization of Breakdown Voltage (BV) Leakage Current AlGaN/GaN High Electron Mobility Transistors (HEMTs) with source‐connected field plate (SCFP) using an Artificial Neural Network (ANN) model. For first time, concept inverse design is applied to HEMT structures, enabling accurate prediction structural parameters from key performance metrics. Additionally, method for predicting current collapse based on peak electric access region proposed, offering faster alternative traditional pulsed DC analysis. The electrical reference device optimized through unique that combines genetic algorithm ANN model, incorporating data augmentation ensure high accuracy. demonstrated exceptional precision, achieving score 99% error rate below 1%. To validate model's predictions, TCAD simulations were performed Pareto‐optimal solutions, yielding minimum 1.67%. work marks significant step forward applying machine learning novel, efficient simulation methods paving way more energy‐efficient process.

Language: Английский

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Application of machine learning in polyimide structure design and property regulation

High Performance Polymers, Journal Year: 2025, Volume and Issue: unknown

Published: Jan. 8, 2025

Polyimide (PI) is widely used in modern industry due to its excellent properties. Its synthesis methods and property research have significantly progressed. However, the design regulation of PI structures through traditional technologies are slow expensive, which make it difficult meet practical demand materials. With rapid development high-throughput computing data-driven technology, machine learning (ML) has become an important method for exploring new Data-driven ML envisaged as a decisive enabler PIs discovery. This paper first introduces basic workflow common algorithms ML. Secondly, applications material properties prediction, assisting computational simulation inverse desired reviewed. Finally, we discuss main challenges possible solutions research.

Language: Английский

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Accurately Models the Relationship Between Physical Response and Structure Using Kolmogorov–Arnold Network

Advanced Science, Journal Year: 2025, Volume and Issue: unknown

Published: Feb. 7, 2025

Artificial intelligence (AI) in science is a key area of modern research. However, many current machine learning methods lack interpretability, making it difficult to grasp the physical mechanisms behind various phenomena, which hampers progress related fields. This study focuses on Poisson's ratio hexagonal lattice elastic network as varies with structural deformation. By employing Kolmogorov-Arnold Network (KAN), transition network's from positive negative element shifts convex polygon concave was accurately predicted. The KAN provides clear mathematical framework that describes this transition, revealing connection between and geometric properties element, identifying parameters at equals zero. work demonstrates significant potential clarify relationships underpin responses behaviors.

Language: Английский

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Density-based topology optimization for ITR-free thermal cloaks

International Journal of Heat and Mass Transfer, Journal Year: 2025, Volume and Issue: 242, P. 126834 - 126834

Published: Feb. 27, 2025

Language: Английский

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Advanced computational techniques: Bridging metaheuristic optimization and deep learning for material design through image enhancement

Elsevier eBooks, Journal Year: 2025, Volume and Issue: unknown, P. 197 - 228

Published: Jan. 1, 2025

Language: Английский

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