FuSe: Precise Sequence Specific Block Copolymers for Directed Self-Assembly - Co-Design of Lithographic Materials for Pattern Quality, Scaling, and Manufacturing

在未来半导体（FuSe）计划的支持下，芝加哥大学的Paul Nealey教授和Juan de Pablo教授，康奈尔大学的Christopher Ober教授和威斯康星大学麦迪逊分校的Whitney Loo教授将在半导体制造的背景下设计，合成和研究用于大批量高分辨率图案的新材料和工艺。光刻或图像化是半导体制造的使能技术。最近，用于最高分辨率光刻的光已经从波长193纳米改为波长13.5纳米的高能极紫外光（EUV）。这种颠覆性的进步使得在更小的尺寸上制造更强大、更快的半导体器件成为可能。该项目将利用材料和工艺共同设计的概念，通过称为EUV +定向自组装（DSA）的策略来增强EUV光刻工艺。在生物和医学领域开发的工具将用于合成含有bcp的多肽，用于EUV和DSA应用的高精度和均匀性增强材料特性。结合图形科学的进步和美国半导体制造业的竞争力，实习计划将为两年制社区大学生提供在大学洁净室操作的实践培训，以推动他们成为高水平的半导体制造技术人员。该项目的重点是设计和合成新型嵌段共聚物（BCP）材料，并将其用于半导体制造的大批量高分辨率euv模式。研究团队将利用材料和工艺共同设计的概念，通过称为EUV +定向自组装（DSA）的策略来增强EUV光刻工艺。为EUV + DSA设计BCP的一个问题是需要一个全面的材料平台，以：1)了解控制高、低N系统的基本新物理，2)在每个目标分辨率下将多个优化的协变属性设计成不同的BCP化学，以及3)确保商业化的强大材料供应链。A-block-(B-random-C)架构将用于将热力学特性（chi, chiN）与表面和界面特性解耦，并允许优化或设计共变特性，如BCP片层周期（分辨率）、块表面能（通过膜域的垂直方向）、域之间的尖锐界面（低线边缘粗糙度）和图案转移能力。本研究的重点是开发基于多肽的bcp。重要的是，基于多肽的嵌段共聚物提供了在B-r-C嵌段中设计序列特异性的机会，以共同设计关键的EUV和DSA特性、表面能、嵌段之间的界面宽度和图案转移。高chi和低N体系不遵循传统的BCP理论和标度定律，将发现和利用多肽体系的新物理特性来优化光刻应用的材料。多肽平台非常适合机器学习方法来优化这些系统的特性和理解这些系统的新物理特性。使用固相合成技术批量生产的序列和组成特定的零分散性bcp将实现实验、理论和计算的前所未有的整合，包括机器学习，以理解和利用图案的紧急行为。该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

With the support of the Future of Semiconductors (FuSe) Program, Professors Paul Nealey and Juan de Pablo at the University of Chicago, Professor Christopher Ober at Cornell University, and Professor Whitney Loo at the University of Wisconsin-Madison will design, synthesize and investigate new materials and processes for high-volume high-resolution patterning in the context of semiconductor manufacturing. Lithography or patterning is the enabling technology for semiconductor manufacturing. Recently the light used for the highest resolution lithography has changed from a wavelength of 193 nano meter to a more energetic extreme ultra-violet (EUV) light with a wavelength of 13.5 nano meter. This disruptive advance enabled patterning at smaller dimensions to manufacture ever more powerful and faster semiconductor devices. This project will capitalize on the concept of co-design of materials and processes to enhance the EUV lithographic process through a strategy known as EUV plus directed self-assembly (DSA). Tools that were developed in the biological and medical sciences will be used here to synthesize polypeptoid containing BCPs for high precision and uniformity of augmented material properties for EUV plus DSA applications. Coupled to the advancement of patterning science and US semiconductor manufacturing competitiveness, an internship program will provide hands-on training in cleanroom operations to 2-year community college students in university cleanrooms in order to propel them into careers as high-level semiconductor manufacturing technicians .The project is focused on the design and synthesis of new block copolymer (BCP) materials and their use for high-volume high-resolution EUV-based patterning for semiconductor manufacturing. The research team will capitalize on the concept of co-design of materials and processes to enhance the EUV lithographic process through a strategy known as EUV plus directed self-assembly (DSA). An issue in designing BCPs for EUV plus DSA is the need for a comprehensive materials platform to: 1) understand the fundamental new physics governing high chi low N systems, 2) engineer multiple optimized covarying attributes into different BCP chemistries at each target resolution, and 3) ensure a robust materials supply chain for commercialization. A-block-(B-random-C) architectures will be employed to decouple thermodynamic properties (chi, chiN) from surface and interfacial properties and to allow for optimized or engineered covarying properties such as BCP lamellar period (resolution), block surface energies (perpendicular orientation of through film domains), sharp interfaces between domains (low line edge roughness), and pattern transfer capabilities. The research is focused on the development of BCPs based on polypeptoids. Importantly, polypeptoid-based block copolymers provide opportunities to engineer sequence specificity in the B-r-C block to co-design key EUV plus DSA properties, surface energy, width of interfaces between blocks, and pattern transfer. High chi and low N systems do not obey traditional BCP theory and scaling laws, and new physics of the polypeptoid systems will be discovered and exploited to optimize materials for the lithographic applications. The polypeptoid platform is ideally suited for machine learning approaches to optimize properties and to understand the new physics of these systems. Sequence and composition specific BCPs with zero dispersity made in quantity using solid-phase synthesis will enable unprecedented integration of experiment, theory, and computation, including machine learning to understand and exploit emergent behavior for patterning.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.