The ever increasing pattern densities and design complexities make the tuning of optical proximity correction (OPC)
recipes more challenging. There are various recipe tuning methods to meet the challenge, such as genetic algorithm
(GA), simulated annealing, and OPC software vendor provided recipe optimizers. However, these methodologies usually
only consider edge placement errors (EPEs). Therefore, these techniques may not provide adequate freedom to solve
unique problems at special geometries, for example bridge, pinch, and process variation band related violations at
complex 2D geometries.
This paper introduces a general methodology to fix specific problems identified at the OPC verification stage and
demonstrates its successful application to two test-cases. The algorithm and method of the automatic scoring system is
introduced in order to identify and prioritize the problems that need to be fixed based on severity, with the POR recipe
score used as the baseline reference. A GA optimizer, whose objective function is based on the scoring system, is
applied to tune the OPC recipe parameters to optimum condition after generations of selections. The GA optimized
recipe would be compared to existing recipe to quantify the amount of improvement.
This technique was subsequently applied to eliminate certain chronic OPC verification problems which were
encountered in the past. Though the benefits have been demonstrated for limited test cases, employing this technique
more universally will enable users to efficiently reduce the number of OPC verification violations and provide robust