As the operating frequency of automated test equipment (ATE) increases, the thermal degradation of the components that constitute the channel accelerates. Degraded components cause signal integrity (SI) issues in the channel, which is a major factor in reducing the test quality and, thus, degrading the reliability of the ATE. Traditionally, test engineers have detected degraded components through direct probing; however, this process is time-consuming and necessitates an automated faulty component diagnosis framework. Accordingly, in this study, we propose a deep learning-based faulty component diagnosis framework to identify components that cause signal quality degradation due to heat in the ATE transmission channel. To analyze the effect of the thermal degradation of individual components on signal quality, a component modeling approach utilizing electromagnetic (EM) simulation was employed to construct a database of S-parameter data based on the temperature of the component. The simulation model demonstrated a high correlation with the measurement waveform data, with an average consistency of 97.1%, thereby ensuring its reliability. Furthermore, to address the issue of data scarcity in industrial environments, a conditional generative adversarial network (CGAN) was developed to generate S-parameter image data. The generated data showed a high similarity to the original S-parameter image data, with an average structural similarity index measure (SSIM) of 0.9845 and a peak signal-to-noise ratio (PSNR) of 35.21 dB. The convolutional neural network (CNN)-based faulty component diagnosis model trained with augmented data exhibited excellent performance, classifying faulty component types with an accuracy of 99.78%.

Semiconductor manufacturing relies heavily on automatic test equipment (ATE), and yet thermal aging poses a critical risk to equipment reliability. This study proposes a novel anomaly detection framework for ATE driver boards by integrating cumulative degradation time (CDT)—derived from accelerated life testing (ALT)—with artificial intelligence models. Specifically, the approach quantifies the cumulative effects of thermal stress as CDT and utilizes it as a key input feature to enable the early detection of degradation under prolonged high-temperature conditions. The proposed framework successfully demonstrates the capability to diagnose real-time anomalies before critical CDT thresholds are reached. Consequently, this approach allows for efficient management, significantly contributing to reduced maintenance costs, minimized downtime, and enhanced equipment reliability, serving as a foundational strategy for condition-based maintenance (CBM) strategies in semiconductor manufacturing.