An innovative Source-Mask co-Optimization (SMO) method for extending low k1 imaging

Abstract

The optimization of the source topology and mask design [1,2] is vital to future advanced ArF technology node development. In this study, we report the comparison of an iterative optimization method versus a newly developed simultaneous source-mask optimization approach. In the iterative method, the source is first optimized based on normalized image log slopes (NILS), taking into account the ASML scanner's diffractive optical element (DOE) manufacturability constraints. Assist features (AFs) are placed under the optimized source, and then optical proximity correction (OPC) is performed using the already placed AFs, in the last step the source is re-optimized using the OPC-ed layout with the AFs. The source is then optimized using the layout from the previous stage based on a set of user specified cost function. The new approach first co-optimizes a pixelated freeform source and a continuous transmission gray tone mask based on edge placement error (EPE) based cost function. ASML scanner specific constraints are applied to the optimized source, to match ASML's current and future illuminator capabilities. Next, AF "seeds" are identified from the optimized gray tone mask, which are subsequently co-optimized with the main features to meet the process window and mask error factor requirement. The results show that the new method offers significant process window improvement.

"Smart" source, mask, and target co-optimization to improve design related lithographically weak spots

“…The advantage of co-optimization over an iterative method is that it is less probable to end up in a local minimum, especially for the case with multiple degrees of freedom for source and mask optimization. It is already known that the co-optimization method provides significant process window improvement over iterative approach [3]. For DRO application, we define the cost function by adding the design related variable into the SMO cost function as:…”

“…We start with the description of the cost function and algorithm in a standard SMO application first. In SMO, the source and mask co-optimization is derived from an intuitive cost function based on edge placement error (EPE) through all evaluation points and all process window conditions [3] by:…”

“…To help address this challenge, the most advanced RETs have been adopted including SMO [1,2,3,4] and MBSRAF (Model-Based Sub-Resolution Assist Features) [5]. However, despite using these advanced RETs, there are still unresolved lithographically weak patterns.…”

EUV source-mask optimization for 7nm node and beyond

“…Tachyon SMO (source-mask optimization) has been a key enabler for the 20 nm node and beyond with 193 nm immersion lithography especially for ASML DOE (diffractive optical element) and FlexRay illumination shape optimization [1,2]. ASML NXE:3300 EUV scanners with a set of standard off-axis illumination pupils have been introduced for the 10 nm node as of 2013 [3,4].…”

Source mask polarization optimization

“…1, 2 SMO, where advanced computational methods are used to design the best simultaneous illumination shape and mask pattern, has found wide spread adoption even more recently. [3][4][5] The combined power of these two innovations allows successful microfabrication of complex integrated circuits at lowest Rayleigh k 1 values with the highest available 193 nm numerical aperture (NA = 1.35). To date, polarization and SMO have been combined in a passive manner: typically SMO calculations are performed with an assumed fixed source polarization grid, usually XY.…”