特殊應用積體電路之良率與最佳品管架構分析

Defect Rate Analysis for the Optimal Quality Control Scheme of Application Specific IC

丁奕如(Yi-Ju Ting)

指導教授 ： 吳政鴻

國立臺灣大學/工學院/工業工程學研究所/碩士(2017年)

https://doi.org/10.6342/NTU201701557

摘要

過去半導體科技產品製造廠商IC不良率品質管制之方法，但多在產品已有實質產出後，觀察產出之不良率異常波動，事後以較被動之手法進行品質管制。本研究之目標以在IC未有實際產出資料前，使用代表IC與其成品物理特性的物理性表徵因子，包含IC與成品的外觀、可耐溫度等性質為解釋變數，預測IC應有之穩定長期不良率；對於品質之動態管制，亦以時間序列分析在實際批量產出前，預先預測批量不良率，在產品產出前預先進行管制。
　　本研究以特殊應用積體電路（ASIC）2015年至2016年，兩年內之不良率歷史資料為例，分四階段進行研究分析：首先以IC的物理性表徵因子為解釋變數，預測IC兩年長期穩定不良率；接著針對批量間不良率之穩定性進行檢定，研究發現有八成的ASIC不論是原始批次IC不良率，或累積批次到固定批量後的IC不良率，皆不穩定、不符合相同二項式分配，批次間不良率有相關性，不適合使用傳統之不良率管制圖；故最後本研究針對批量IC不良率有時間相關性的ASIC，以批量不良率歷史時間序列，建構自迴歸移動平均模型(ARIMA model)預測下一批量IC之不良率，並根據建構出之模型，以時間序列統計管制圖進行品質管制。
　　根據本研究之模型結果，特定單一IC可以依據製程相關因子1與設計相關因子3預測其長期不良率，且模型的解釋力最高達60%。另外亦有一半以上的ASIC可以使用歷史批量不良率資料預測下一批量不良率，並根據預測值進行更主動的動態品質管制，以此取代傳統品質管制方法需被動等待生產異常出現的缺點。另外此方法可以排除趨勢性的不良率變動，因此可以真正動態找出品質異常的批次。

關鍵字

相關性分析 ； 假設檢定 ； 二項式分配 ； 不良率分析 ； 自迴歸移動平均模型 ； 統計品質管制

並列摘要

This research studies the quality of Application Specific Integrated Circuit (ASIC) using historical defective data from 2015 to 2016 provided by a leading ASIC manufacturing company. Conventionally, IC makers adopt Statistical Process Control methods passively for monitoring the defect rate of their IC products. The objective of this study is to use physical characterization factors of the components, which are usually known in the design phase, to predict the long-term defect rates before the actual production data can be collected. The physical characterization factors include the appearance of the component, temperature tolerance, and packaging types.
This research is conducted in four stages. First, the physical characterization factors of IC are used to predict the long-term system assembly component defect rate (known as DPPM) across different ASIC’s. Then, we test the stationarity of the DPPM of one ASIC between its shipping lots. To make the test trustworthy, we accumulate the usage number from consecutive lots up to 10000 to become one batch. As a result, batch-to-batch (the batches sorted by manufacturing time) behavior of more than 80% of the ASIC’s does not follow identical binomial distributions. It means that most of the ASIC’s do not have a stationary DPPM distribution between either lots or batches. As a consequence, there shall exist auto-correlations of the DPPM from consecutive lots. Thus, traditional SPC methods will not be able to perform well in terms of catching the anomalies. In order to catch the auto-correlated behavior, Autoregressive Integrated Moving Average (ARIMA) models are built and analyzed to study the DPPM trends of individual ASIC. Finally, time-series quality control charts are constructed and monitored based on the best-fit ARIMA models.
With the four-stage analysis proposed in this thesis, we are able to provide the guidelines on the observable characteristics of the ASIC’s in order to apply different control and monitoring schemes. Based on our study results, specific single components will be predicting their long-term DPPM according to their process factor 1 and design factor 3, and the power of this predictive model up to 60%. Moreover, half of the ASIC can also use the historical production data to predict batch DPPM in the future. The abnormal points can literally be found after trendy DPPM changes were eliminated. Consequently, we can replace the traditional quality management and make a more active dynamic quality control.

並列關鍵字

Correlation Analysis ； Test for Homogeneity ； Binomial Distribution ； Defect Rate Analysis ； Autoregressive Integrated Moving Average (ARIMA) Model ； Statistical Quality Control

參考文獻

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