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

FuSe：用于定向自组装的精确序列特定嵌段共聚物 - 用于图案质量、缩放和制造的光刻材料的协同设计

• 批准号: 2329133
• 负责人: Paul Nealey
• 金额: $ 192.5万
• 依托单位: University of Chicago
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-11-01 至 2026-10-31
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2329133&HistoricalAwards=false
• 关键词: FuSe; Precise; Sequence; Specific; Block

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.

在半导体未来（FuSe）计划的支持下，芝加哥大学的 Paul Nealey 和 Juan de Pablo 教授、康奈尔大学的 Christopher Ober 教授以及威斯康星大学麦迪逊分校的 Whitney Loo 教授将设计、合成和研究半导体制造中用于大批量高分辨率图案化的新材料和工艺。 光刻或图案化是半导体制造的支持技术。 最近，用于最高分辨率光刻的光已从波长 193 纳米变为更具能量的极紫外 (EUV) 光，波长为 13.5 纳米。 这一颠覆性的进步使得能够以更小的尺寸进行图案化，以制造更强大、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，通过一种称为 EUV 加定向自组装 (DSA) 的策略来增强 EUV 光刻工艺。 生物和医学领域开发的工具将用于合成含有 BCP 的多肽，以实现 EUV 加 DSA 应用的增强材料特性的高精度和均匀性。 结合图案科学的进步和美国半导体制造竞争力，实习计划将为大学洁净室中的两年制社区学院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。该项目专注于新型嵌段共聚物 (BCP) 材料的设计和合成，及其在半导体制造中基于 EUV 的大批量高分辨率图案化的应用。 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。 为 EUV 加 DSA 设计 BCP 的一个问题是需要一个全面的材料平台来：1）了解控制高 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共变属性设计到不同的 BCP 化学中，以及3）确保商业化的强大材料供应链。 A-块-（B-随机-C）架构将用于将热力学性质（chi、chiN）与表面和界面性质解耦，并允许优化或设计共变性质，例如BCP层状周期（分辨率）、块表面能（穿过薄膜域的垂直方向）、域之间的清晰界面（低线边缘粗糙度）和图案转移能力。 该研究重点是基于多肽的 BCP 的开发。重要的是，基于多肽的嵌段共聚物提供了设计 B-r-C 嵌段中的序列特异性的机会，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理论和比例定律，并且将发现和利用多肽系统的新物理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属性并了解这些系统的新物理原理。使用固相合成技术批量生产的零分散性的序列和成分特定 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紧急行为进行图案化。该奖项反映了 NSF 的法定使命，并被视为值得通过使用基金会的智力优点和更广泛的影响审查标准进行评估来支持。