US 11,900,042 B2
Stochastic-aware lithographic models for mask synthesis
Kevin Dean Lucas, Austin, TX (US); Yudhishthir Prasad Kandel, Durham, NC (US); Ulrich Welling, Dornach (DE); Ulrich Karl Klostermann, Munich (DE); and Zachary Adam Levinson, Austin, TX (US)
Assigned to Synopsys, Inc., Sunnyvale, CA (US)
Filed by Synopsys, Inc., Mountain View, CA (US)
Filed on Nov. 9, 2021, as Appl. No. 17/522,574.
Claims priority of provisional application 63/112,733, filed on Nov. 12, 2020.
Prior Publication US 2022/0146945 A1, May 12, 2022
Int. Cl. G06F 30/30 (2020.01); G03F 7/00 (2006.01); G06F 30/398 (2020.01); G03F 1/36 (2012.01); G03F 1/70 (2012.01)
CPC G06F 30/398 (2020.01) [G03F 1/36 (2013.01); G03F 1/70 (2013.01); G03F 7/705 (2013.01); G03F 7/70433 (2013.01); G03F 7/70441 (2013.01)] 19 Claims
OG exemplary drawing
 
1. A method comprising:
accessing a mask pattern for use in a lithography process that prints a pattern on a wafer; and
applying, by a processor, the mask pattern to a deterministic model of the lithography process to predict a characteristic of the printed pattern that is subject to local stochastic variations, comprising:
applying the mask pattern to a deterministic compact model that predicts the characteristic of the printed pattern, wherein the compact model does not account for local stochastic variations of the characteristic; and
applying a deterministic correction to the predicted characteristic from the compact model, wherein the correction accounts for the local stochastic variations of the characteristic in the printed pattern.