US 12,461,450 B2
OPC modeling method
Yinuo Pan, Zhejiang (CN); Yingfang Wang, Shanghai (CN); Keeho Kim, Hartford, CT (US); Norman S. Chen, Ballston Spa, NY (US); and Eric S. Parent, Ballston Spa, NY (US)
Assigned to HEFECHIP CORPORATION LIMITED, Hong Kong (CN)
Filed by HeFeChip Corporation Limited, Hong Kong (CN)
Filed on Dec. 7, 2022, as Appl. No. 18/076,814.
Claims priority of application No. 202211111853.3 (CN), filed on Sep. 13, 2022.
Prior Publication US 2024/0192609 A1, Jun. 13, 2024
Int. Cl. G03F 7/00 (2006.01); G03F 1/70 (2012.01); G06F 30/30 (2020.01); G03F 1/36 (2012.01); G06F 111/06 (2020.01)
CPC G03F 7/70441 (2013.01) [G03F 1/70 (2013.01); G03F 7/705 (2013.01); G06F 30/30 (2020.01); G03F 1/36 (2013.01); G06F 2111/06 (2020.01)] 11 Claims
OG exemplary drawing
 
1. An optical proximity correction (OPC) modeling method, comprising:
step S1: determining optical model parameters, resist model parameters, as well as respective value ranges of the optical model parameters and the resist model parameters;
step S2: obtaining a plurality of parameter combinations by stochastically choosing values for the optical model parameters and the resist model parameters from the respective value ranges, wherein each parameter combination contains values of both the optical model parameters and the resist model parameters, and each parameter combination is different from any other parameter combination;
step S3: performing photolithography simulations and etching wafers using the plurality of parameter combinations, calculating root mean square (RMS) values of differences between simulated critical dimensions (CDs) and etching CDs, and calculating Bossung curve error (BCE) values of the CDs;
step S4: evaluating the RMS values and the BCE values according to Pareto principle and calculating a Pareto optimum set, a Pareto suboptimum set and second-best to N-th-best Pareto suboptimum sets, to prioritize the plurality of parameter combinations in a descending order, where N is an integer greater than 1;
step S5: applying a genetic algorithm with position-based crossover and/or mutation to the plurality of parameter combinations prioritized in step S4, to obtain a plurality of new parameter combinations; and
step S6: iterating steps S3 to S5 on the plurality of new parameter combinations until a number of iterations reaches a first predetermined value, and using highest prioritized ones of parameter combinations resulting from a last iteration for OPC modeling.