Semiconductor Residue Deposition Monitoring in Exhaust Pipeline Based on Electrical Capacitance Tomography and Convolution Neural Network

Abstract

Toxic and corrosive by-products generated during semiconductor manufacturing can accumulate inside exhaust pipes, forming solid residues that pose risks such as pipe clogging, reduced pumping efficiency, and operational accidents. To mitigate these risks, regular preventive maintenance (PM) is required, highlighting the need for nondestructive, real-time monitoring technologies to ensure process efficiency and worker safety. This study proposes a data-driven approach to estimate the free volume index (FVI) and internal deposit conditions using electrical capacitance measurements. Two convolutional neural network (CNN) architectures 1-D convolutional neural network (1D-CNN) and 2-D convolutional neural network (2D-CNN) were developed and trained on simulated capacitance data under various deposition scenarios. The methodology was first evaluated through numerical simulations to test robustness and the performance was benchmarked against a fully connected neural network (FCNN). Subsequently, the approach was validated using real capacitance data collected from an operating semiconductor facility, thereby confirming its practical applicability. The proposed CNN-based method demonstrated high accuracy, robustness to noise, and strong generalization, offering a practical solution for early detection of clogging and process anomalies. This work contributes toward safer and efficient semiconductor manufacturing through intelligent pipe condition monitoring.