臺灣博碩士論文加值系統

本研究利用二極體固態雷射切割機，應用於QFN(Quad Flat Non-lead)積體電路晶片封裝材料之切割，取代傳統鑽石鋸刀(Diamond-impregnated Saws)切割。並利用類神經網路來建構一個切割品質的預測模式，此模式以雷射的電流、頻率及切割速度為主要的切割輸入參數，來推導出6個雷射切割品質輸出，包含樹脂和樹脂及Cu的切割深度、熱效應的寬度及切割線的寬度。在類神經網路部分，使用倒傳遞網路(back-propagation, BP)中的Levenberg-Marquardt (LM)演算法作為學習運算法則，來發展出預測QFN的雷射切割品質的預測模式，以期達到最快收斂、較高的精密度及最佳的執行效果。在類神經網路的訓練部分，使用27組的不同切割參數組合，共162組數據來進行訓練網路，得到一個數學的預測模式，再將所得到的預測數據與實際的實驗數據進行比對，得到的平均誤差為1.69%；在類神經網路的測試部分，隨機選取14種組合，共84組數據代入此數學預測模式，獲得的預測數據，與實際的實驗數據進行比對，得到的平均誤差為2.91%。並利用此數學預測模式，找到最佳切割參數，其參數值為33A的電流、2.23 kHz的頻率和3.2 mm/s的切割速度。經上述的實驗，由本研究所建立的QFN的雷射切割品質的預測模式，可以有效的預測出QFN的雷射切割品質，期待在雷射相關產業，能廣泛的應用。

This paper reports a new application of artificial neural networks (ANN) to build a predictive model for six laser cutting qualities of QFN (Quad Flat Non-lead) packages. For the ANN, the back-propagation (BP) with Levenberg-Marquardt (LM) algorithm is used to obtain better performance, fast convergence and accurate predictive ability. From the experimental results, the average predicting error compared experimental data for training and testing processes are 1.69% and 2.91%, respectively. By using the ANN predictive model, the combination of optimal cutting parameters that produces the smallest width of HAZ in the complete cutting is found and the laser cutting parameters are current of 33A, frequency of 2.23k Hz and cutting speed of 3.2 mm/s. The results show that the ANN model has the predictive ability to estimate six laser cutting qualities for QFN package accurately. And the ANN applied to this paper is very successfully and may give guides in the predictions of cutting QFN packages and is expected to be useful for laser applications in other industry fields.

中文摘要
Abstract
誌謝
Contents
List of Figures
List of Tables
1. Introduction
2. QFN packages and laser cutting system
3. Six laser cutting qualities for QFN Packages
3.1 Available Power output from DPSSL system
3.2 Main laser cutting parameters
3.3 Experimental data for six laser cutting qualities
4. Artificial neural network
4.1 Levenberg-Marquardt algorithm (LM)
4.2 The structure of the ANN
5. Results and discussions
5.1 The training process
5.2 The testing process
5.3 The combination of optimal cutting parameters
6. Conclusion
Reference
作者簡介
授 權 書

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