Fully Automated Inverse Co-Optimization of Templates and Block Copolymer Blending Recipes for Directed Self-Assembly Lithography

The directed self-assembly (DSA) of block copolymers (BCPs) offers a highly promising approach for the fabrication of contact holes or vertical interconnect access at sub-10 nm technology nodes. To fabricate circular nanoholes with precisely controlled size and positions, the self-assembly of block copolymers requires guidance from a properly designed template. Effectively parametrizing the template shape to enable efficient optimization remains a critical yet challenging problem. Moreover, the optimized template must possess excellent manufacturing capabilities for practical applications. In this work, we propose a Gaussian descriptor for characterizing the template shape with only two additional parameters by making full use of the positional parameters of the nanoholes. We further propose using AB/AB binary blends instead of pure diblock copolymer to improve the adaptability of the block copolymer system to the template shape. The Bayesian optimization (BO) is applied to co-optimize the binary blend and the template shape. Our results demonstrate that BO based on the Gaussian descriptor can efficiently yield the optimal templates for diverse multihole patterns, all leading to highly matched self-assembled morphologies. Moreover, by imposing constraints on the variation of the curvature of the template during optimization, superior manufacturability is ensured for each optimized template. It is noteworthy that each key parameter of the blend exhibits a relatively wide tunable window under the requirement of a rather high precision. Our work provides valuable insights for advancing DSA technology and thus potentially propels its practical applications forward.