Lithography S
P
E
C
I
A
L
F
O
C
U
S
Optimizing Yield By Detecting Lithography and Etch CD Process Excursions by Richard C. Elliott, Raman K. Nurani, Sung Jin Lee, Luis Ortiz, Moshe Preil, KLA-Tencor Corporation, J. George Shanthikumar, University of California at Berkeley, Trina Riley, Greg Goodwin, Advanced Micro Devices
Effectively detecting lithography and etch critical dimension (CD) process excursions while minimizing added cost can have a significant impact on semiconductor production yield. Finding this balance requires effective application-specific planning in order to identify excursions and find the optimal measurement scheme. There are many different yield-limiting excursion signatures in photo and etch, and a given excursion signature at photo may turn into a different excursion signature at etch with a different impact on yield and performance. Many current sampling plans and monitoring schemes miss these excursions. An improved procedure for effective detection of CD process excursions can have a significant impact on yield and revenue.
Feature dimension is a critical parameter for lithography and etch processes in semiconductor manufacturing. CD measurements are made for pass/fail purposes to ensure that the data for a particular lot are within the process tolerances. These tolerances are usually specified in terms of basic statistics such as the lot mean and range. The data is also used to identify systematic trends in the process over time. If necessary, the lot CD measurements can be fed back manually or automatically to adjust the process. The measurement sampling required to precisely estimate the mean CD of the lot is a function of the baseline process variations. For example, in a process that has minimal wafer-to-wafer variation, the measurement of multiple wafers per production lot does not greatly improve the estimate of the lot mean CD. Determining baseline variations requires accurate estimation of different variance components such as lot-to-lot variation, wafer-to-wafer variation within a lot, fieldto-field variation within a wafer, and siteto-site variation within a field7. It is common practice to use a nested ANOVA model to compute these variations3,6. 60
Spring 2000
Yield Management Solutions
However, current ANOVA models do not provide accurate estimates of these variations when systematic variations are present in the data. A new Generalized ANOVA model is more effective than the conventional ANOVA model for characterizing the baseline process variations. The full distribution of CD measurements can also be used to identify isolated process failures or “excursions.� While process excursions that are isolated to within field or within wafer may not greatly affect the mean CD of an entire production lot, they can have a catastrophic impact on the performance or yield of the semiconductor devices. Identifying these excursions is critical to ensure timely correction of yield limiting lithography and etch process issues. This requires a precise estimation of the systematic and random components of the total variation (otherwise some of the random excursions can be masked under the total variation). The guiding principle to the approach outlined in this article is to determine a sampling plan that effectively detects process excursions, while minimizing the metrology resources required to support the collection of this data. These resources include not only the capital cost of the CD measurement equipment, but also the engineering resources required to analyze and interpret the data, and the lost production time which occurs when metrology data erroneously indicates the occurrence