I-Corps: LabMate: Accelerated Empirical Process Optimization

I-Corps：LabMate：加速经验流程优化

• 批准号: 1623032
• 负责人: Roger Bonnecaze
• 金额: $ 5万
• 依托单位: University of Texas at Austin
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2016
• 资助国家: 美国
• 起止时间: 2016-01-15 至 2017-04-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1623032&HistoricalAwards=false
• 关键词: Corps; LabMate; Accelerated; Empirical; Process

Today, recipe development and optimization for micro- and nanofabrication processes are typically based on time consuming and expensive experimental trial and error. Some processes may take up to a year to be created and fully optimized severely limiting technology development. Statistical software tools like JMP attempt to capitalize on classical design of experiment (DoE) techniques to mitigate this high cost of recipe creation and optimization, but they neglect information that might be gained from an understanding of process physics and often require large numbers of experiments for precise fits. Other process tools, including Synopsys and Coventor, lack many predictive capabilities. Shortening the development cycle offers a clear opportunity to save time and money and enables new nanoscale technologies. This I-Corps team has invented a methodology for using physics based models and integrated Bayesian statistics to dramatically speed up and reduce the cost for the empirical optimization of micro- and nanofabrication processes compared to classical DoE. The proposed technology "LabMate" employs an iterative feedback between a model constructed on a robust theoretical foundation and experiments. In addition, LabMate enables the knowledge and experience of the user to be incorporated quantitatively into the model to further decrease the time to optimization and making it well-suited for commercial application. Preliminary application of the proposed method to the development of dry etching recipes shows that the number of experiments can be reduced by a factor of two to three compared to the number of experiments required by DoE. This translates to hundreds of thousands of dollars in annual savings for etch recipe creation and optimization. Importantly, although the initial target market lies within the semiconductor space, the proposed technology can easily translate to any physical process with a large number of unknown parameters and limited experimental data. This team is targeting recipe optimization for dry etching processes at companies including LAM, Intel, Tokyo Electron, Global Foundries, Taiwan Semiconductor Manufacturing Company, and Applied Materials. An initial market survey taught the team that the few simulation tools that exist today for etch recipe creation and optimization lack the flexibility and capacity of our invention. The team will employ a subscription based model with customer support to commercialize the proposed software.

如今，微纳米纤维工艺的配方开发和优化通常基于耗时且昂贵的实验试错。有些流程可能需要长达一年的时间才能创建和充分优化，这严重限制了技术开发。JMP等统计软件工具试图利用经典的实验设计（DoE）技术来减轻配方创建和优化的高成本，但它们忽略了可能从过程物理学的理解中获得的信息，并且通常需要大量的实验来进行精确拟合。包括Synopsys和Coventor在内的其他流程工具缺乏许多预测能力。缩短开发周期为节省时间和金钱提供了一个明确的机会，并使新的纳米技术成为可能。这个I-Corps团队发明了一种方法，使用基于物理的模型和集成的贝叶斯统计，与经典DoE相比，大大加快和降低了微纳米制造工艺的经验优化成本。所提出的技术“LabMate”采用了一个强大的理论基础和实验上构建的模型之间的迭代反馈。此外，LabMate还可以将用户的知识和经验定量地整合到模型中，进一步缩短优化时间，使其非常适合商业应用。初步应用所提出的方法的干法蚀刻配方的发展表明，实验的数量可以减少了一个因素的两个到三个相比，能源部所需的实验的数量。这意味着每年可为蚀刻配方的创建和优化节省数十万美元。重要的是，虽然最初的目标市场位于半导体领域，但所提出的技术可以很容易地转化为具有大量未知参数和有限实验数据的任何物理过程。该团队的目标是为包括LAM、英特尔、东京电子、全球代工厂、台湾半导体制造公司和应用材料在内的公司进行干法蚀刻工艺的配方优化。最初的市场调查告诉团队，目前存在的用于蚀刻配方创建和优化的少数模拟工具缺乏我们发明的灵活性和能力。该团队将采用基于订阅的模式，并提供客户支持，以使拟议的软件商业化。