Fully automated inverse co-optimization of templates and block copolymer blending recipes for DSA lithography

TL;DR

Addressing sub-7 nm DSA lithography, the paper introduces a fully automated inverse co-optimization of guiding templates and AB/AB block copolymer blends. It proposes a two-parameter Gaussian descriptor for the template and uses Bayesian optimization to jointly optimize $tau$, $nu$ and blend parameters to match target multi-hole patterns. AB/AB blends extend the design space, producing templates with smoother curvature and improved manufacturability compared with neat AB, while maintaining high pattern fidelity. The co-optimization achieves high-precision morphologies for diverse layouts, including quintuple-hole L-arrangements, and with curvature constraints demonstrates practical manufacturability for VIA-level patterns.

Abstract

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-7nm technology nodes. To fabricate circular holes with precisely controlled size and positions, the self-assembly of block copolymers requires guidance from a properly designed template. Effectively parameterizing the template shape to enable efficient optimization remains a critical yet challenging problem. Moreover, the optimized template must possess excellent manufacturability for practical applications. In this work, we propose a Gaussian descriptor for characterizing the template shape with only two parameters. We further propose to use 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 multi-hole patterns, all leading to highly matched self-assembled morphologies. Moreover, by imposing constraints on the variation of 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 rather high precision. Our work provides valuable insights for advancing DSA technology, and thus potentially propels its practical applications forward.

Figures (10)

• Figure 1: Template optimization with pure AB diblock: self-assembled morphologies inside the optimal template shapes. The black lines indicate the template boundaries, the blue outer circles represent the size of target holes, the blue inner circles indicate the target hole centers, and the red crosses mark the actual centers of the A-block domains (same notations apply below).
• Figure 2: Optimize the template or the blend for targeting the double-hole pattern with $d=5.00R_g$. (a) the self-assembled morphology of pure AB diblock copolymer in the optimized template. (b) the self-assembled morphology of the AB/AB blend in the optimized template. (c) the self-assembled morphology of the optimized AB/AB blend in a specific template. (d) visualization of the multi-objective function in the 3D parameter space of the AB/AB blend: The color represents the logarithm of the objective function value, with darker color indicating lower (better) objective function value.
• Figure 3: Spatial distribution of the free end of the long B-block from the second diblock copolymer in the binary blend.
• Figure 4: (a) position deviation in an ideal case (b) size deviation in an ideal case. (c) parameter window that satisfies the conditions $\mathcal{L}_{\rm pos} \le 1.2\times 10^{-3}$ and $\mathcal{L}_{\rm cir} \le 2.5\times 10^{-3}$, where circular dots represent feasible points whose $\mathcal{L}_{\rm total}$ is indicated by color spectrum, whereas blue crosses indicate non-compliant points.
• Figure 5: Representative two-dimensional parameter windows: (a) $f_{\rm A_1}$-$f_{\rm B_2}$ parameter window at $\phi_1=0.60$. (b) $\phi_1$-$f_{\rm B_2}$ parameter window at $f_{\rm A_1}=0.28$. (c) $\phi_1$-$f_{\rm A_1}$ parameter window at $f_{\rm B_2}=1.40$.