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<p>Advanced semiconductor packaging technology has become a key enabler of high-performance systems in the post-Moore era. However, thermo-mechanical reliability issues, such as warpage and thermal resistance, have emerged as critical challenges due to increased structural complexity and material heterogeneity. These issues stem from nonlinear interactions among material properties, structural geometries, and process conditions. Although finite element analysis (FEA) provides high predictive accuracy, its application is limited by significant computational cost and time demands, particularly in design exploration and optimization. This review presents a comprehensive analysis of machine learning-based approaches for predicting thermo-mechanical reliability challenges, specifically warpage and thermal resistance, in advanced semiconductor packaging. For each reliability issue, predictive methodologies employing a range of machine learning models are summarized, and recent research trends are examined with emphasis on data acquisition strategies, model architectures, and application cases. In addition, extended approaches encompassing data augmentation, design optimization, and model interpretability are also reviewed. The discussion section addresses key challenges such as model generalization, data scarcity, interpretability, and integration into real-world manufacturing environments, based on the technical background and limitations common to the two reliability issues. Building on this analysis, future research directions are proposed, including physics-informed learning, multi-physics-aware modeling, and hybrid frameworks combining simulation and experimental data. In conclusion, machine learning-based approaches offer a computationally efficient pathway for analyzing thermo-mechanical behavior in advanced semiconductor packaging, enabling design space exploration at speeds infeasible with conventional FEA alone. Looking ahead, these methods are expected to play a pivotal role in design optimization and reliability assessment.</p><p></p>

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A comprehensive review of machine learning approaches for predicting warpage and thermal resistance in advanced semiconductor packaging

  • 期刊论文

  • 发表日期：

    2026年6月3日

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  • Jeongwoo Park,
  • Dongwoo Kim,
  • Sangheon Yang,
  • Minjong Kim,
  • Hongyun So

摘要

Advanced semiconductor packaging technology has become a key enabler of high-performance systems in the post-Moore era. However, thermo-mechanical reliability issues, such as warpage and thermal resistance, have emerged as critical challenges due to increased structural complexity and material heterogeneity. These issues stem from nonlinear interactions among material properties, structural geometries, and process conditions. Although finite element analysis (FEA) provides high predictive accuracy, its application is limited by significant computational cost and time demands, particularly in design exploration and optimization. This review presents a comprehensive analysis of machine learning-based approaches for predicting thermo-mechanical reliability challenges, specifically warpage and thermal resistance, in advanced semiconductor packaging. For each reliability issue, predictive methodologies employing a range of machine learning models are summarized, and recent research trends are examined with emphasis on data acquisition strategies, model architectures, and application cases. In addition, extended approaches encompassing data augmentation, design optimization, and model interpretability are also reviewed. The discussion section addresses key challenges such as model generalization, data scarcity, interpretability, and integration into real-world manufacturing environments, based on the technical background and limitations common to the two reliability issues. Building on this analysis, future research directions are proposed, including physics-informed learning, multi-physics-aware modeling, and hybrid frameworks combining simulation and experimental data. In conclusion, machine learning-based approaches offer a computationally efficient pathway for analyzing thermo-mechanical behavior in advanced semiconductor packaging, enabling design space exploration at speeds infeasible with conventional FEA alone. Looking ahead, these methods are expected to play a pivotal role in design optimization and reliability assessment.