應用類神經網路於蝕刻製程之缺陷分析與預測

Abstract

現今半導體製程中，包含上千道的製程步驟，以及參數。要如何有效的監控與偵測製程參數異常，製程監控機制就扮演著不可或缺的角色。為了要提高產量及良率，降低製程缺陷之發生，監控製程的每一步驟是否異常，藉由製程參數之錯誤偵測(Defect Detection)，以及預先一步預測缺失的發生，來達到此一目標。本論文之目的即在氧化物反應性離子蝕刻(Reactive Ion Etching, RIE)製程中，發展一套能夠即時監控，並且預測下一批貨或是下一個製程步驟之製程參數有無出現異常之方法。來解決上述提及之問題。

在本論文中，利用主成分分析(Principle Component Analysis, PCA)以及類神經網路(Neural Network, NN)來分析製程參數資料，製程因子先經由主成分分析處理後，得到主要成分，再送入針對RIE製程參數所建立完成之類神經網路，預測其是否出現異常；根據實際的製程需求及情況，可將RIE製程分為兩部分來建立其製程監控模型，第一部份利用全部製程步驟之資訊，來偵測參數異常狀況，稱之為離線(Offline)錯誤偵測模組；另一部份利用前三個製程步驟即時資訊來預測第四個製程步驟之異常，稱之為即時(Online)錯誤偵測模組。 經由實際製程參數資料及上述兩個模組實驗驗證後，確實能夠偵測及預測出製程參數之異常。同時，藉由此監控機制，可減少當製程異常時，排除異常的時間，並且達到低成本即時控制之應用。

The fabrication of modern semiconductor products requires thousands of processing steps. A key element in achieving high yields during semiconductor fabrications is to minimize the amount of defected wafers. Therefore, detecting the defected wafers and predicting the wafer status are very important issues. In this study, BPNN is the backbone of prediction models and the BPNN inputs were prepared in three different ways: raw data by using the first set of data points, capturing samples from the original data, and the statistical summary values by calculate the mean and standard deviation values for each step. Four prediction models are established to predict the wafer status: offline back-propagation neural network (BPNN), offline principle component analysis BPNN (PCABPNN), online BPNN, and online PCABPNN. These models have the potential to reduce the overall cost of ownership of semiconductor equipment by increasing the wafer yield and throughput of product wafers, and not depend upon monitor wafers or expensive metrology rather it will enable inexpensive real-time wafer-to-wafer control applications in RIE. This study establishes a method for deciding the significant process parameters which affect the wafer status in RIE by comparing the result of applying each process parameter alone in BPNN. The significant parameters for all etching steps combined together in offline BPNN to tackle the defected wafer. Furthermore the significant parameters for the first three etching steps combined together in online BPNN to forecast the wafer status. By modifying the significant parameters when online BPNN model predicts the defected wafer, the down-time and mean-time-to-repair of the equipment can be decreased. The evaluation results for the four models demonstrate that each model has its advantages and disadvantages under different BPNN input preparations. However, preparing statistical summary as BPNN inputs has less error prediction. Therefore, using statistical summary in online PCABPNN is recommended to enable rapid prediction of wafer status in RIE which greatly reducing test wafer necessity.