Closed Loop Digitalised Data Analytics and Analysis Platform (DAAP) for Intelligent Design and Manufacturing of Power Electronic Modules

用于电力电子模块智能设计和制造的闭环数字化数据分析平台（DAAP）

• 批准号: EP/W006642/1
• 负责人: Stoyan Stoyanov
• 金额: $ 40.91万
• 依托单位: University of Greenwich
• 依托单位国家: 英国
• 项目类别: Research Grant
• 财政年份: 2022
• 资助国家: 英国
• 起止时间: 2022 至 无数据
• 项目状态: 未结题
• 来源: https://gtr.ukri.org/projects?ref=EP%2FW006642%2F1
• 关键词: Closed; Loop; Digitalised; Data; Analytics

Power electronic modules (PEMs) and higher-level systems play an increasingly important role in adjustable-speed drives, unified power quality correction, utility interfaces with renewable energy resources, energy storage systems, electric or hybrid electric vehicles and more electric ship/aircraft. The power electronic technologies provide compact and high-efficient solutions to power conversion but deployment of power electronic modules in such applications comes with challenges for their reliable and safe operation.This project aims to address four key challenges which the power electronics manufactures, and PEM end-users continue to face:Challenge 1: No in-line and non-destructive inspection methods for PEM package quality and internal integrity assessment (wire bonds, die attachment and encapsulant) embedded within the production line.Challenge 2: No comprehensive PEM data on design-quality-reliability characteristics, no processes for chartreisation and test data integration and management, and for data modelling and analysis.Challenge 3: No advanced capabilities for accurate assessment of PEM deployment risks and for lifetime management.Challenge 4: No or limited data is fed back from end-users to PEM designers/manufacturers, no application-informed design and manufacturing quality. The project seeks to develop a digitalised Data Analytics and Analysis Platform (DAAP) for PEMs. The following novel and beyond current state-of-art developments in the project address the above stated challenges:1) Non-Destructive Testing (NDT) with real-time data acquisition capability. A novel technique for NDT using LF-OCT imaging will be enhanced and optimised to provide quality data for individual PEMs. The proposed NDT method can quantitatively measure the mechanical deformation of gel-encapsulated bonding wires down to nanometer level. It can capture an entire cross-sectional image without any mechanical scanning, providing novel capability of running in-line with the packaging process.2) Quality Predictions using AI and Machine Learning (ML): Research on integration and use of multiple data formats and sources, including standard datasets of electrical parameter test measurements, image data from in-line LF-OCT, and off-line X-ray and other imaging techniques, will be undertaken. The integrated data will underpin the accurate and automated quality evaluation of each individual PEM by enabling the development of ML and Deep Learning models. The modelling capability will enable packaging quality evaluations based on comprehensive sets of design and packaging process attributes.3) Reliability Predictions. Current state-of-art in design-reliability and in-service degradation modelling for PEMs will be advanced through the proposed inclusion of manufacturing quality characteristics and design attributes in the reliability predictions. This will result in enhanced knowledge and more accurate, quality-informed reliability modelling and insights into the relations between design, quality and reliability by analytics of manufacturing and end-user data.4) Data-Modelling-Optimisation Capabilities' Integration. The proposed integration (DAAP) of data, information exchange, and different modelling capabilities with multi-objective optimisation methods will be a novel development. The proposed optimisation routines will provide new capabilities for power semiconductor packaging design (e.g. module architecture, materials, interconnect solutions, application-specific reliability performance, etc.) and optimal process control on the manufacturing line.

电力电子模块 (PEM) 和更高级别的系统在可调速驱动、统一电能质量校正、与可再生能源的公用设施接口、储能系统、电动或混合动力汽车以及更多电动船舶/飞机方面发挥着越来越重要的作用。电力电子技术为功率转换提供了紧凑、高效的解决方案，但在此类应用中部署电力电子模块对其可靠和安全运行提出了挑战。该项目旨在解决电力电子制造和 PEM 终端面临的四个关键挑战-用户继续面临：挑战 1：没有针对生产线中嵌入的 PEM 封装质量和内部完整性评估（焊线、芯片附件和密封剂）的在线和无损检测方法。挑战 2：没有关于 PEM 封装的全面 PEM 数据设计-质量-可靠性特征，没有图表化和测试数据集成和管理以及数据建模和分析的流程。挑战 3：没有准确评估 PEM 部署风险和生命周期管理的高级功能。挑战 4：没有数据或数据有限从最终用户反馈给 PEM 设计者/制造商，没有基于应用的设计和制造质量。该项目旨在为 PEM 开发数字化数据分析和分析平台 (DAAP)。该项目中以下新颖且超越当前最先进的发展解决了上述挑战：1）具有实时数据采集功能的无损检测（NDT）。使用 LF-OCT 成像的 NDT 新技术将得到增强和优化，以为单个 PEM 提供高质量数据。所提出的无损检测方法可以定量测量凝胶封装键合线的机械变形，直至纳米级。它可以捕获整个横截面图像，无需任何机械扫描，提供与包装过程一致运行的新颖功能。2）使用人工智能和机器学习（ML）进行质量预测：研究多种数据格式的集成和使用以及将采用各种来源，包括电参数测试测量的标准数据集、来自在线 LF-OCT 的图像数据以及离线 X 射线和其他成像技术。集成数据将支持机器学习和深度学习模型的开发，从而支持对每个 PEM 进行准确和自动化的质量评估。建模功能将能够基于全面的设计和包装过程属性集进行包装质量评估。3) 可靠性预测。通过提议在可靠性预测中纳入制造质量特性和设计属性，将推进当前 PEM 设计可靠性和使用中退化建模的最新技术。这将增强知识和更准确、基于质量的可靠性建模，并通过分析制造和最终用户数据来深入了解设计、质量和可靠性之间的关系。4) 数据建模优化功能的集成。所提出的数据、信息交换和不同建模能力与多目标优化方法的集成（DAAP）将是一个新颖的发展。所提出的优化例程将为功率半导体封装设计（例如模块架构、材料、互连解决方案、特定应用的可靠性性能等）和生产线上的最佳过程控制提供新功能。

项目成果

Damage Mechanics-Based Failure Prediction of Wirebond in Power Electronic Module

• DOI: 10.1109/access.2023.3342689
• 发表时间: 2024
• 期刊: IEEE Access
• 影响因子: 3.9
• 作者: P. Rajaguru;T. Tilford;Chris Bailey;S. Stoyanov