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和Juan de巴勃罗教授、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教授将设计、合成和研究用于半导体制造的大批量高分辨率图案化的新材料和工艺。 光刻或图案化是用于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193纳米的波长改变为具有13.5纳米的波长的更高能的极紫外（EUV）光。 这一突破性的进步使得在更小的尺寸上进行图案化成为可能，从而制造出更强大、更快的半导体器件。 该项目将利用材料和工艺协同设计的概念，通过称为EUV加定向自组装（DSA）的策略来增强EUV光刻工艺。 在生物和医学科学中开发的工具将用于合成含有BCP的类多肽，以实现EUV加DSA应用的增强材料性能的高精度和均匀性。 再加上图案化科学的进步和美国半导体制造业的竞争力，一个实习项目将在大学洁净室为两年制社区学院的学生提供洁净室操作的实践培训，以推动他们成为高级半导体制造技术人员。该项目的重点是新型嵌段共聚物（BCP）材料的设计和合成，以及它们在大批量高性能半导体制造中的应用。用于半导体制造的基于EUV的高分辨率图案化。 研究团队将利用材料和工艺协同设计的概念，通过一种称为EUV加定向自组装（DSA）的策略来增强EUV光刻工艺。 设计用于EUV加DSA的BCP的问题是需要一个全面的材料平台来：1）理解管理高chi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到不同的BCP化学中，以及3）确保用于商业化的稳健的材料供应链。 A-嵌段-（B-无规-C）结构将用于将热力学性质（chi，chiN）与表面和界面性质解耦，并允许优化或工程化的共变性质，例如BCP层状周期（分辨率）、嵌段表面能（贯穿膜域的垂直取向）、域之间的尖锐界面（低线边缘粗糙度）和图案转移能力。 该研究的重点是基于多肽的BCP的开发。重要的是，基于类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加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系统不遵守传统的BCP理论和标度定律，并且将发现和利用多肽系统的新物理来优化用于光刻应用的材料。 多肽类平台非常适合机器学习方法，以优化属性并了解这些系统的新物理学。利用固相合成技术大量生产零分散度的序列和成分特定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，以理解和利用模式化的涌现行为。该奖项反映了NSF的法定使命，并通过使用基金会的智力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.