結合集群分析與資料視覺於低良率晶圓之成因探討

Causing factor analysis of low-yield wafer using clustering and data visualization

賈方霈

指導教授 ： 孫天龍

元智大學/工程學院/工業工程與管理學系/碩士(2002年)

摘要

產品良率對於企業或工廠而言，是衡量其是否具競爭優勢的重要指標。而現行工廠中在面對產品良率的掌控問題時，大多是藉由製程工程師對製程的瞭解，以經驗挑出可能影響良率的製程參數，再以實驗設計或配合實驗批量的生產，來驗證所選擇之參數是否確實為影響良率的主因。但繁複的晶圓製程中會產生大量的製程參數，且參數間又具有相互的影響關係，故以傳統統計方法分析時困難度較高。因此，本研究將以集群分析配合資料視覺，從晶圓廠LPC（lot in-line process control）資料中，找出影響晶圓良率為低的製程參數。本研究之方法首先是將LPC資料轉換成描述製程品質特徵（process quality feature，PQF）的集合，透過PQF指標的集群分析及視覺化，來粹取對良率具影響力的關鍵PQF指標，再利用這些關鍵PQF指標對晶圓批量進行集群分析後，分析相似的晶圓批量良率表現，研究結果驗證了有相似PQF指標的批量，的確呈現了相同良率之表現。

關鍵字

資料探勘 ； 資料視覺 ； 集群分析 ； IC不良率成因探討

並列摘要

Yield improvement is critical for an IC manufacturing company to remain competitive in the market. High-yield products not only bring more profit to the company but also indicate better manufacturing skills. Current practice toward yield improvement relies on engineers to select candidate manufacturing parameters and use statistical techniques such as design of experiment to verify the hypotheses. This approach is difficult and less effective due to the large number of parameters and the complicated interactions among them. This research employees clustering and visualization technique to help engineers find possible causing factors of low-yield wafers from the lot in-line process control (LPC) data, which are collected from the metrology machine during IC fabrication. We convert the raw LPC data into process quality features (PQF). With the help of clustering & visualization, the key PQF’s are selected. These key PQF’s are then used to cluster the wafer lots. By analysis these low-yield wafer lots that are grouped into the same cluster, i.e., having similar PQF’s, manufacturing parameters causing the low-yield wafers could be identified.

並列關鍵字

data mining ； data visualization ； clustering ； causing factor analysis of low-yield wafer

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