SHF: Small: Perception-Based Analytics For Semiconductor Production and Test Data

SHF：小型：针对半导体生产和测试数据的基于感知的分析

• 批准号: 2006739
• 负责人: Li-Chung Wang
• 金额: $ 46.11万
• 依托单位: University of California-Santa Barbara
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-10-01 至 2024-09-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2006739&HistoricalAwards=false
• 关键词: SHF; Small; Perception; Based; Analytics

Big data analytics is transforming multiple industries by impacting how companies operate their businesses. The semiconductor industry, a key driver for the high-growth technology sector, is in the vanguard of this development. A large part of data-analytics practice in the semiconductor industry relies on workflows that involve human-in-the-loop steps to make an expert decision. How to automate those human-in-the-loop steps remains a fundamental challenge, and requires capturing the decision-making perception of an expert into software. This project will develop a set of novel methods and software tools that provide the capability for a domain expert to effectively model their perception in an analytics workflow. This capability enables automation of the workflow. Workflow automation not only enhances productivity but also reduces the chance of human error and, consequently, improves the overall quality of an operation. The experimental framework developed in the project will serve as an education platform for undergraduate and graduate students with diverse backgrounds. The platform will support training across multiple fields, including semiconductor production and test, machine learning, and software engineering. Such training provides a broader choice of career paths for students and prepares them with skills to meet future demands in industry.Data analytics in the semiconductor industry largely involves prescriptive analytics, where analytics results are often used to support decision making that demands accountability. Today, there remains a substantial gap between what data-analytics and machine-learning technologies can provide and what is needed for automating a prescriptive analytics workflow. This project will fill the gap by developing a perception-based analytics software component which enables domain experts to model their perception into an automated workflow. The project addresses two fundamental challenges in such modeling. The first concerns the lack of sufficient data samples when training a model. The second concerns the requirements for robustness when using such a model. To address the first challenge, the project will pursue a novel approach that enables learning a model in one domain via learning a model in another domain that has sufficient data samples readily available for training. To address the second challenge, the project will provide an innovative approach enabling integration of multiple learning methods to achieve a required robustness level. The values and impacts of the research results will be demonstrated based on data collected from actual semiconductor product lines and through experiments that reflect actual industrial settings.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.

大数据分析正在通过影响公司运营业务的方式来改变多个行业。半导体行业是高增长技术领域的关键驱动力，是这一发展的先锋。半导体行业的大部分数据分析实践都依赖于涉及人工参与的工作流程来做出专家决策。如何自动化这些人在回路中的步骤仍然是一个根本性的挑战，并且需要将专家的决策感知捕获到软件中。该项目将开发一套新颖的方法和软件工具，为领域专家提供在分析工作流程中有效建模其感知的能力。此功能可实现工作流的自动化。工作流程自动化不仅提高了生产力，还减少了人为错误的机会，从而提高了操作的整体质量。该项目中开发的实验框架将作为具有不同背景的本科生和研究生的教育平台。该平台将支持多个领域的培训，包括半导体生产和测试、机器学习和软件工程。半导体行业的数据分析主要涉及规范性分析，分析结果通常用于支持需要承担责任的决策制定。如今，数据分析和机器学习技术所能提供的与自动化规范性分析工作流程所需的之间仍然存在巨大差距。这个项目将填补差距，开发一个基于感知的分析软件组件，使领域专家能够将他们的感知建模成一个自动化的工作流程。该项目解决了这种建模中的两个基本挑战。第一个问题是在训练模型时缺乏足够的数据样本。第二个问题涉及使用这种模型时对鲁棒性的要求。为了解决第一个挑战，该项目将采用一种新的方法，通过学习一个领域中的模型来学习另一个领域中的模型，该领域具有足够的数据样本可用于训练。为了应对第二个挑战，该项目将提供一种创新的方法，使多种学习方法的整合，以达到所需的鲁棒性水平。研究成果的价值和影响将通过实际半导体生产线收集的数据和反映实际工业环境的实验来证明。该奖项反映了NSF的法定使命，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。

项目成果

Language Driven Analytics for Failure Pattern Feedforward and Feedback

• DOI: 10.1109/itc50671.2022.00037
• 发表时间: 2022-09
• 期刊: 2022 IEEE International Test Conference (ITC)
• 作者: Min Jian Yang;Y. Zeng;Li-C. Wang

MINiature Interactive Offset Networks (MINIONs) for Wafer Map Classification

用于晶圆图分类的 MINIature 交互式偏移网络 (MINION)

• DOI: 10.1109/itc50571.2021.00027
• 发表时间: 2021
• 期刊: 2021 IEEE International Test Conference (ITC
• 作者: Zeng, Yueling Jenny;Wang, Li-C.;Shan, Chuanhe Jay
• 通讯作者: Shan, Chuanhe Jay

Wafer Map Pattern Analytics Driven By Natural Language Queries

• DOI: 10.1109/itcasia55616.2022.00016
• 发表时间: 2022-08
• 期刊: 2022 IEEE International Test Conference in Asia (ITC-Asia)
• 作者: Y. Zeng;Min Jian Yang;Li-C. Wang

Learning A Wafer Feature With One Training Sample

使用一个训练样本学习晶圆特征

• DOI: 10.1109/itc44778.2020.9325254
• 发表时间: 2020
• 期刊: 2020 IEEE International Test Conference (ITC
• 作者: Zeng, Yueling Jenny;Wang, Li-C.;Shan, Chuanhe Jay;Sumikawa, Nik
• 通讯作者: Sumikawa, Nik