表面黏著技術製程是透過印刷鋼網，由印刷機將錫膏印刷於電路板焊盤上，再經由錫膏檢查機確認錫膏印刷狀態。之後再藉由實裝機吸取零件，經過機器判別其外型是否符合規格，符合規格的零件就會被精確地裝著於電路板印刷位置上，再經過迴焊爐，將零件熔接於電路板後，形成半成品。
近年來因人口老化、少子化及工廠人員流動率高等因素，造成勞動人力短缺及技術流失之困境。所以，如何將印刷機老師傅調整印刷參數的經驗及技術，透過機器學習及深度學習的方式，取得較佳錫膏厚度並建立預測錫膏厚度模組,透過此模組能精確地讓工程師預測錫膏厚度，這是一個非常重要的課題，也是本論文將研究及實現的主題。
本論文將取得實際SMT生產線上，由數十個機種經錫膏檢查機檢查輸出的錫膏厚度資料。從各種印刷參數組合所得到之錫膏厚度為大數據，透過深度學習來建立預測錫膏厚度的模型。讓SMT生產線工程師可於模型內輸入印刷參數，用以預測錫膏厚度，來達到未來開發模組的目標。

關鍵字：表面黏著，檢查機，錫膏，鋼網，實裝機

The surface mounting technology (SMT) process involves printing solder paste onto the circuit board pads through a stencil by a printing machine, and then confirming the printing status of the solder paste by a solder paste inspection machine (SPI). After that, the parts are picked up by the mounter machine, and the machine judges whether the shape of the parts meets the specifications or not. Parts that meet the specifications are precisely mounted on the printed circuit boards. Finally, the parts are fused to the circuit board through a soldering reflow to form a semi-finished product.
In recent years, due to the aging population, childlessness, and high turnover rate of factory workers, there is a shortage of labor manpower. Therefore, how to present the experience and technology of printing machine masters in a digital way and predict the thickness of solder paste accurately is an important topic. This is also the theme that this paper will study and realize.
In this paper, we obtain the solder thickness data from dozens of products in an actual SMT production line through solder paste inspection. The solder paste thickness obtained from various combinations of printing parameters is used as the big data to build a model for predicting the solder paste thickness through deep learning. This allows SMT line engineers to input printing parameters into the model to predict solder paste thickness to achieve the goal of developing modules in the future.

Key words: SMT, SPI, solder, mask, mounter

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