0 citations0 references

Optimizing OPC data sampling based on "orthogonal vector space"

Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE2011Vol. 7973, pp. 79732K–79732K

Citations Over Time

Yuyang Sun, Yee Mei Foong, Yingfang Wang, Jacky Cheng, Dongqing Zhang, Shaowen Gao, Nanshu Chen, Byoung Il Choi, Antoine Bruguier, Mu Feng, Jianhong Qiu, S. Hunsche, Liang Liu, W. Shao

Abstract

With shrinking feature sizes and error budgets in OPC models, effective pattern coverage and accurate measurement become more and more challenging. The goal of pattern selection is to maximize the efficiency of gauges used in model calibration. By optimizing sample plan for model calibration, we can reduce the metrology requirement and modeling turn-around time, without sacrificing the model accuracy and stability. With the Tachyon pattern-selection-tool, we seek to parameterize the patterns, by assessing dominant characteristics of the surroundings of the point of interest. This allows us to represent each pattern with one vector in a finite-dimensional space, and the entire patterns pool with a set of vectors. A reduced but representative set of patterns can then be automatically selected from the original full set sample data, based on certain coverage criteria. In this paper, we prove that the model built with 56% reduced wafer data could achieve comparable quality as the model built with full set data.

Related Papers

→ Accurate roughness measurements by dynamic calibration, VFM-uncertainty calculations and a special calibration specimen(2005)5 cited
→ Verification of wafer-level calibration accuracy at high temperatures(2008)3 cited
→ Verification of wafer-level calibration accuracy at high temperatures(2008)3 cited
→ Transportability of a laser-induced flourescence calibration for no at high pressure(1995)9 cited
UNCERTAINTY EVALUATION OF CALIBRATION SPECIFICATION FOR PRESSURE REFERENCE LEAK(2015)