| 摘要: | 摘 要 DRAM為整個IT產業乃至於整個高科技產業，具有舉足輕重的地位，其所包含的領域相當的廣泛，從電腦、手機、光碟機、數位相機、印表機…等數位產品。而DRAM已經是一種標準化的產品，並無法像其他的半導體IC可以藉由產品規格來穩定客戶，僅能不斷的降低生產成本才能提升利潤。降低生產成本的方式大抵為減少生產週期、引進先進製程，以及提升產品的良率。 良率的提升對半導體產業是相當重要的。以個案公司為例，個案公司每年約產出10億顆記憶體晶粒，良率提升1% 表示個案公司於一樣的生產條件下，不增加資本投入，每年便可多出了1,000萬顆記憶體晶粒，以每顆記憶體晶粒為2塊美金計算，整年度之盈餘便可多出2千萬美金。因此，除了透過上述的降低產品生產週期以及引進先進製程外，各半導體DRAM製造廠無不極力的利用各種方式提升產品良率，提升獲利。 目前，個案公司為更進一步的提升產品良率，將以均勻度係數為預測晶圓良率指標，控管製程、提升良率。然而就理論上而言，均勻度係數是可以做為預測晶圓良率指標，但利用生產過程所產生之量測資料計算出均勻度係數是否可以真正反映出製程的穩定性卻是未知的。因此，本研究將以個案公司生產過程中所產生之量測資料計算出之均勻度係數，藉此與良率進行統計分析及相關分析以驗証均勻度係數是否可做為預測晶圓良率指標。 本研究發現，無論以任何一種角度分析均勻度係數與良率的關係，甚至深入研究較顯著的蝕刻模組，結果均顯示雖然有少數機台之均勻度係數與良率有較高的相關性，但是絕大部份的群組是無相關性。因此本研究的結論為：均勻度係數於個案公司不適合做為製程穩定性指標。 Abstract DRAM plays a central role in the electronics industry due to its wide spread applications in various products, like PC, mobile phone, optical disk drive, digital camera, printer, digital TV, etc. However, DRAM is a standardized product, and is treated as a commodity in the market. Thus, a DRAM manufacturer cannot keep its customer with unique product specifications like other semiconductor ICs. The most important issue in improving profitability is to reduce its production costs. In general, there are three methods to reduce its production costs: reducing manufacturing cycle time, introducing advanced technology and improving yield. Yield improvement is very important for semiconductor industry. The company in this study produces about 1,000 million chips per year. If the yield rate can be improved by 1%, it translates to an additional 10 million finished chips per year without further investment. Assuming the selling price of a chip to be 2 USD, it can result in additional 20 million USD per year in profit for the company. Thus, every DRAM foundry tries their best to find any possible means to improve its yield rate. Among the various plans that the company in this study attempts, is to improve its production uniformity, which is a statistics collected for daily quality management purpose, hoping that it would improve yield. It sounds logical to use uniformity as an indicator for predicting yield, but whether it is an effective indicator in practice is not known. Thus, this study attempts to investigate the feasibility by statistically analyzing the data collected previously, to establish any relationship between yield and uniformity. Results of the in depth analysis show that uniformity alone, as collected for quality management purpose, is not an effective predictor of yield. Therefore, we do not recommend using the uniformity statistics as a predictor of yield. |
