Paper
20 April 2011 Automatic optimization of metrology sampling scheme for advanced process control
Chuei-Fu Chue, Chun-Yen Huang, Chiang-Lin Shih
Author Affiliations +
Abstract
In order to ensure long-term profitability, driving the operational costs down and improving the yield of a DRAM manufacturing process are continuous efforts. This includes optimal utilization of the capital equipment. The costs of metrology needed to ensure yield are contributing to the overall costs. As the shrinking of device dimensions continues, the costs of metrology are increasing because of the associated tightening of the on-product specifications requiring more metrology effort. The cost-of-ownership reduction is tackled by increasing the throughput and availability of metrology systems. However, this is not the only way to reduce metrology effort. In this paper, we discuss how the costs of metrology can be improved by optimizing the recipes in terms of the sampling layout, thereby eliminating metrology that does not contribute to yield. We discuss results of sampling scheme optimization for on-product overlay control of two DRAM manufacturing processes at Nanya Technology Corporation. For a 6x DRAM production process, we show that the reduction of metrology waste can be as high as 27% and overlay can be improved by 36%, comparing with a baseline sampling scheme. For a 4x DRAM process, having tighter overlay specs, a gain of ca. 0.5nm on-product overlay could be achieved, without increasing the metrology effort relative to the original sampling plan.
© (2011) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Chuei-Fu Chue, Chun-Yen Huang, and Chiang-Lin Shih "Automatic optimization of metrology sampling scheme for advanced process control", Proc. SPIE 7971, Metrology, Inspection, and Process Control for Microlithography XXV, 79711O (20 April 2011); https://doi.org/10.1117/12.879375
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KEYWORDS
Metrology

Overlay metrology

Semiconducting wafers

Scanners

Process control

Manufacturing

Data modeling

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