OPC strategies to reduce failure rates with rigorous resist model stochastic simulations in EUVL

Stochastic effects are the ultimate limiters of optical lithography and are a major concern for next-generation technology nodes. In previous work, we compared the performance of several types of EUV resists on dense patterns or brightfield mask SRAM cells across technology nodes. It was shown that due to low photon/chemical shot noise and reduced blur, metal-oxide resists could potentially reduce lithography failures at the 5nm technology node though even at 7nm technology node failures may be noticeable if process variations are considered. Following up on work published the last three years, in this paper we study how different OPC strategies and photoresist properties might affect failure rates for a darkfield mask SRAM cell at the 5nm technology node. Four cases are considered: • Aerial image optimization by mask biasing; stochastic simulations are performed with an organic chemically amplified resist model. • Aerial image optimization by model-based OPC; stochastic simulations are performed with an organic chemically amplified and a metal-oxide resist model. • Aerial image model-based OPC enhanced by rigorous stochastic modeling; stochastic simulations are performed with an organic chemically amplified resist model. In all cases, a numerical aperture of 0.33 is used. Process windows are generated averaging ~2150 (3.5σ) stochastic simulations for each focus-dose combination, while best focus-dose target CDs are found by analyzing failure rates across focus and dose. Roughly 1.8 million (5σ) trials are then run at best condition for all cases to quantify part per million failures.