2008
DOI: 10.1117/12.772274
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Optimization of high order control including overlay, alignment, and sampling

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Cited by 11 publications
(4 citation statements)
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“…However, the sensitivity to the process variability remains as a hurdle to be tackled and part of this is compensated by the overlay reading after the pattern printing. With the adoption of the high order correction on the scanner, the overlay control improves up to 100% comparing to the baseline linear method [2][3] and the measurement points are grown from 2 to 4-digits sampling. Accordingly, throughput improvement on overlay measurement has been driven, too.…”
Section: Introductionmentioning
confidence: 99%
“…However, the sensitivity to the process variability remains as a hurdle to be tackled and part of this is compensated by the overlay reading after the pattern printing. With the adoption of the high order correction on the scanner, the overlay control improves up to 100% comparing to the baseline linear method [2][3] and the measurement points are grown from 2 to 4-digits sampling. Accordingly, throughput improvement on overlay measurement has been driven, too.…”
Section: Introductionmentioning
confidence: 99%
“…In this paper, the impact of TMU on overlay error correction, which includes process and measurement noise, is investigated in terms of the stability of high-order correction parameters. [3][4][5][6] By evaluating the variation range of correctable parameters as a figure of merit, a practical TMU is determined for a given design rule. Our methodology is described and our simulation results are discussed.…”
Section: Introductionmentioning
confidence: 99%
“…In recent years, the industry has moved toward high-order overlay modeling and more sophisticated alignment strategies, which requires more overlay sampling and excessive alignment [7][8][9][10][11]. For example, the work in [11] suggests high-order process control by overlay control with one model per lot or one model for every wafer; the work in [7] proposes high-order wafer alignment, while the work in [9] proposes exposure tool characterization using offline overlay sampling. These improvements in overlay control are capable of reducing overlay errors considerably (by up to 30% [7,9]) when a high-order overlay model is used.…”
Section: Introductionmentioning
confidence: 99%