SPI特徴量を用いたデータ中心機械学習によるPCB製造欠陥検出
Detecting Manufacturing Defects in PCBs via Data-Centric Machine Learning on Solder Paste Inspection Features
はんだペースト検査(SPI)の特徴量を用いて、ピン・部品・基板の各レベルで機械学習モデルを訓練し、PCB製造の欠陥を検出するデータ中心アプローチを提案した。
著者: Jubilee Prasad-Rao, Roohollah Heidary, Jesse Williams
分類: cs.LG, cs.AI, cs.CV, cs.RO
原文アブストラクト
Automated detection of defects in Printed Circuit Board (PCB) manufacturing using Solder Paste Inspection (SPI) and Automated Optical Inspection (AOI) machines can help improve operational efficiency and significantly reduce the need for manual intervention. In this paper, using SPI-extracted features of 6 million pins, we demonstrate a data-centric approach to train Machine Learning (ML) models to detect PCB defects at three stages of PCB manufacturing. The 6 million PCB pins correspond to 2 million components that belong to 15,387 PCBs. Using a base extreme gradient boosting (XGBoost) ML model, we iterate on the data pre-processing step to improve detection performance. Combining pin-level SPI features using component and PCB IDs, we developed training instances also at the component and PCB level. This allows the ML model to capture any inter-pin, inter-component, or spatial effects that may not be apparent at the pin level. Models are trained at the pin, component, and PCB levels, and the detection results from the different models are combined to identify defective components.