2024

Vol.30 No.4

Editorial Office

Review

  • Journal of the Microelectronics and Packaging Society
  • Volume 30(4); 2023
  • Article

Review

Journal of the Microelectronics and Packaging Society 2023;30(4):69-78. Published online: Feb, 20, 2024

MAGICal Synthesis: Memory-Efficient Approach for Generative Semiconductor Package Image Construction

  • Yunbin Chang1 , Wonyong Choi2 , and Keejun Han1†
    1School of Computer Engineering, Hansung University, 116, Samseongyo-ro 16-gil, Seongbuk-gu, Seoul, Republic of Korea, 2R&D Center, Genesem, 24, Songdogwahak-ro 84beon-gil, Yeonsu-gu, Incheon, Republic of Korea
Corresponding author E-mail: keejun.han@hansung.ac.kr
Abstract

With the rapid growth of artificial intelligence, the demand for semiconductors is enormously increasing everywhere. To ensure the manufacturing quality and quantity simultaneously, the importance of automatic defect detection during the packaging process has been re-visited by adapting various deep learning-based methodologies into automatic packaging defect inspection. Deep learning (DL) models require a large amount of data for training, but due to the nature of the semiconductor industry where security is important, sharing and labeling of relevant data is challenging, making it difficult for model training. In this study, we propose a new framework for securing sufficient data for DL models with fewer computing resources through a divide-and-conquer approach. The proposed method divides high-resolution images into pre-defined sub-regions and assigns conditional labels to each region, then trains individual sub-regions and boundaries with boundary loss inducing the globally coherent and seamless images. Afterwards, full-size image is reconstructed by combining divided sub-regions. The experimental results show that the images obtained through this research have high efficiency, consistency, quality, and generality.

Keywords Data Augmentation, Generative Adversarial Networks, Artificial Intelligence, Performance Optimization

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