A Multimodal Convolutional Neural Network Framework for Intelligent Real-Time Monitoring of Etchant Levels in PCB Etching Processes

Chuen-Sheng Cheng (), Pei-Wen Chen, Hen-Yi Jen and Yu-Tang Wu
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Chuen-Sheng Cheng: Department of Industrial Engineering and Management, Yuan Ze University, No. 135, Yuan-Tung Road, Chung-Li District, Taoyuan City 32003, Taiwan
Pei-Wen Chen: Department of Industrial Engineering and Management, Yuan Ze University, No. 135, Yuan-Tung Road, Chung-Li District, Taoyuan City 32003, Taiwan
Hen-Yi Jen: Department of Industrial Engineering and Management, Yuan Ze University, No. 135, Yuan-Tung Road, Chung-Li District, Taoyuan City 32003, Taiwan
Yu-Tang Wu: Department of Industrial Engineering and Management, Yuan Ze University, No. 135, Yuan-Tung Road, Chung-Li District, Taoyuan City 32003, Taiwan

Mathematics, 2025, vol. 13, issue 17, 1-16

Abstract: In recent years, machine learning (ML) techniques have gained significant attention in time series classification tasks, particularly in industrial applications where early detection of abnormal conditions is crucial. This study proposes an intelligent monitoring framework based on a multimodal convolutional neural network (CNN) to classify normal and abnormal copper ion (Cu 2+ ) concentration states in the etching process in the printed circuit board (PCB) industry. Maintaining precise control Cu 2+ concentration is critical in ensuring the quality and reliability of the etching processes. A sliding window approach is employed to segment the data into fixed-length intervals, enabling localized temporal feature extraction. The model fuses two input modalities—raw one-dimensional (1D) time series data and two-dimensional (2D) recurrence plots—allowing it to capture both temporal dynamics and spatial recurrence patterns. Comparative experiments with traditional machine learning classifiers and single-modality CNNs demonstrate that the proposed multimodal CNN significantly outperforms baseline models in terms of accuracy, precision, recall, F1-score, and G-measure. The results highlight the potential of multimodal deep learning in enhancing process monitoring and early fault detection in chemical-based manufacturing. This work contributes to the development of intelligent, adaptive quality control systems in the PCB industry.

Keywords: multimodal convolutional neural network (CNN); printed circuit board; etching process; recurrence plot (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2025
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