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<p>Surface Mount Technology (SMT) has transformed modern electronics manufacturing, enabling the production of compact, reliable, and high-performance devices. Among the critical phenomena in SMT, the self-alignment effect during reflow soldering plays a pivotal role in ensuring precise component placement through the principles of energy minimization. Despite its importance, current research primarily focuses on numerical predictions of self-alignment metrics, leaving a significant gap in generating visual representations, such as Post-Automated Optical Inspection (AOI) images, to comprehensively depict alignment outcomes. In this study, we introduce a novel framework that combines physics-based modeling with advanced deep learning to address this gap. Leveraging data from Solder Paste Inspection (SPI), Pre-Automated Optical Inspection (Pre-AOI), and Post-Automated Optical Inspection (Post-AOI) stages, we developed an energy minimization-based physics model to produce displacement vectors capturing self-alignment dynamics. We integrated these inputs, along with key component attributes, into PhyViT-GAN, a Physics-Guided Variational Autoencoder, and MobileViT as the discriminator in adversarial training for generating high-fidelity Post-AOI images. Our approach effectively visualizes the self-alignment effect, achieving a Structural Similarity Index Measure (SSIM) of 0.8348 and a Learned Perceptual Image Patch Similarity (LPIPS) score of 0.0917, demonstrating high structural accuracy and perceptual fidelity. This work represents the first application of deep learning to generate Post-AOI images for visualizing self-alignment in SMT. This framework bridges the gap between physics-driven predictions and visual inspections, offering transformative potential for SMT manufacturing. It enables enhanced defect detection, virtual prototyping, and process optimization, laying the foundation for future advancements in automated and data-driven electronics assembly.</p>

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PhyViT-GAN: Physics-Guided MobileViT-GAN for precise self-alignment image generation

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年1月7日

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  • Manav Barot,
  • Jaewoo Kim,
  • Daehan Won,
  • Sang Won Yoon

摘要

Surface Mount Technology (SMT) has transformed modern electronics manufacturing, enabling the production of compact, reliable, and high-performance devices. Among the critical phenomena in SMT, the self-alignment effect during reflow soldering plays a pivotal role in ensuring precise component placement through the principles of energy minimization. Despite its importance, current research primarily focuses on numerical predictions of self-alignment metrics, leaving a significant gap in generating visual representations, such as Post-Automated Optical Inspection (AOI) images, to comprehensively depict alignment outcomes. In this study, we introduce a novel framework that combines physics-based modeling with advanced deep learning to address this gap. Leveraging data from Solder Paste Inspection (SPI), Pre-Automated Optical Inspection (Pre-AOI), and Post-Automated Optical Inspection (Post-AOI) stages, we developed an energy minimization-based physics model to produce displacement vectors capturing self-alignment dynamics. We integrated these inputs, along with key component attributes, into PhyViT-GAN, a Physics-Guided Variational Autoencoder, and MobileViT as the discriminator in adversarial training for generating high-fidelity Post-AOI images. Our approach effectively visualizes the self-alignment effect, achieving a Structural Similarity Index Measure (SSIM) of 0.8348 and a Learned Perceptual Image Patch Similarity (LPIPS) score of 0.0917, demonstrating high structural accuracy and perceptual fidelity. This work represents the first application of deep learning to generate Post-AOI images for visualizing self-alignment in SMT. This framework bridges the gap between physics-driven predictions and visual inspections, offering transformative potential for SMT manufacturing. It enables enhanced defect detection, virtual prototyping, and process optimization, laying the foundation for future advancements in automated and data-driven electronics assembly.