隨著互聯網時代來臨，穿戴裝置的需求不斷增加，穿戴裝置的可靠度問題隨之備受重視。在穿戴裝置中精密電路的品質，在任何製造過程中的瑕疵都影響著穿戴裝置的準確度及敏感度，都會直接影響產品的可靠度。 為此，本研究選定某廠牌穿戴裝置，在可靠度迴流測試時出現的異常現象為藍本；從魚骨圖的分析結果歸納出異常原因；主要為電鍍製程中微蝕槽作用異常，導致電鍍銅與底銅結合力不佳。為了解決這個問題，本研究是利用振盪感測晶片監控微蝕槽振盪機構，進一步探討微蝕槽的振盪條件對生產品質的影響。 實驗得出振盪頻率⩾40 Hz，振盪開/關秒數20/10sec，可達到孔底最佳蝕刻條件，並通過分析所收集到的振盪機構的振盪數據，成功地量化設備行程的振盪參數，繪成六段不同頻率之設備故障趨勢圖。 本研究透過分析和探討微蝕槽振盪條件參數，除了可以解決電鍍銅與底銅結合力的問題，同時可智能化設備，應用上可實現故障預警，即時發現潛在衰弱跡象，降低停機風險，提高生產效率和產品品質。 ;With the advent of the Internet era, the demand for wearable devices has been continuously increasing, making the reliability of these devices a critical concern. The quality of precision circuits in wearable devices is crucial, as any defects during the manufacturing process can directly impact the accuracy and sensitivity of the device, ultimately affecting its overall reliability. To address these concerns, this research focused on a specific brand of wearable device that exhibited anomalies during reliability loop testing. Through the analysis of a fishbone diagram, the main cause of the anomalies was identified, which was the abnormal functioning of the micro-etching groove in the electroplating process, resulting in poor adhesion between the electroplated copper and the base copper. To resolve this issue, the study utilized an oscillation sensing chip to monitor the oscillation mechanism of the micro-etching groove and further investigated the impact of the micro-etching groove′s oscillation conditions on production quality. The experiment determined that an oscillation frequency of ≥40 Hz with an on/off duration of 20/10 seconds achieved optimal etching conditions at the hole bottom. By analyzing the collected oscillation data from the oscillation mechanism, the research successfully quantified the oscillation parameters of the equipment during six different frequency trips, presenting them in a trend chart for equipment failures. Through the analysis and exploration of the micro-etching groove′s oscillation conditions, the research not only addressed the issue of poor adhesion between electroplated copper and the base copper but also enabled the intelligent application of equipment failure prediction. This approach facilitates real-time detection of potential weakening signs, reducing downtime risks, and improving production efficiency and product quality.
