Inverse lithography recipe optimization using genetic algorithm

Inverse lithography is most commonly utilized to guide optimizing RET (Resolution Enhancement Technology) solution such as SRAF (Sub-resolution Assist Feature) placement and OPC (Optical Proximity Correction) edge dissection and fragmentation. Inverse lithography recipe often features an array of user-controlled parameters, which allow refined tuning of inverse solution maximizing for various common objectives such as common DOF, NILS, and pvband (process variability band). In practice, some test case shows that the inverse lithography engine can be perturbed to produce novel solution maximizing other unconventional objectives such as OPC solution’s mask-friendliness, OPC convergence at extreme dense-to-iso transition, and multi-structure common focus range. However, the parameter tuning process can be time-consuming and requires expert knowledge of the tool. Also, parameters correlation to those objectives is often highly non-linear. All these reasons make inverse lithography recipe parameter tuning for unconventional objective a non-trivial task. Genetic Algorithm (GA) has been demonstrated to be effective at solving non-trivial optimization tasks such as SRAF rule optimization [1], OPC recipe optimization [2,3] and source mask optimization [4-6], and. Here we propose to use modified GA based engine for inverse lithography recipe optimization. We will show experimental results and discuss the benefits and challenges. We will demonstrate with three real test cases that this flow has a reasonable TAT and improved inverse lithography solutions.