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

电力电子模块(PEM)和更高级别的系统在变速驱动、统一电能质量校正、与可再生能源、储能系统、电动或混合动力汽车和更多电动船舶/飞机的实用接口方面发挥着越来越重要的作用。电力电子技术为电力变换提供了紧凑而高效的解决方案，但电力电子模块在这种应用中的可靠和安全运行面临着挑战。本项目旨在解决电力电子制造和PEM最终用户继续面临的四个关键挑战：挑战1：没有对生产线中嵌入的PEM封装质量和内部完整性评估(引线键合、芯片附件和封装剂)进行在线和无损检测的方法。挑战2：没有关于设计-质量-可靠性特性的全面的PEM数据，没有用于表征和测试数据集成和管理的过程，以及数据建模和分析。挑战3：没有用于准确评估PEM部署风险和生命周期管理的高级能力。挑战4：没有或有限的数据从最终用户反馈给PEM设计者/制造商，没有了解应用情况的设计和制造质量。该项目旨在为新兴市场开发一个数字化数据分析和分析平台(DAAP)。该项目中的以下新的和超越当前最先进的发展解决了上述挑战：1)具有实时数据采集能力的无损检测(NDT)。一种使用低频OCT成像进行无损检测的新技术将得到增强和优化，以提供单个PEM的高质量数据。所提出的无损检测方法可以定量测量胶封键合焊丝的机械变形，达到纳米级。它可以在不进行任何机械扫描的情况下捕获整个横截面图像，提供与包装工艺同步运行的新颖能力。2)使用人工智能和机器学习(ML)进行质量预测：将进行多种数据格式和来源的集成和使用研究，包括电气参数测试测量的标准数据集、在线LF-OCT的图像数据，以及离线X射线和其他成像技术。整合的数据将通过支持ML和深度学习模型的开发，为每个PEM的准确和自动化的质量评估奠定基础。建模能力将使包装质量评估基于一套全面的设计和包装工艺属性。3)可靠性预测。通过在可靠性预测中纳入制造质量特征和设计属性的建议，将推进PEM在设计可靠性和在役退化建模方面的最新水平。这将通过分析制造和最终用户数据，提高知识和更准确、更准确的可靠性建模，并深入了解设计、质量和可靠性之间的关系。4)数据建模-优化能力的集成。拟议的数据、信息交换和不同建模能力与多目标优化方法的集成(DAAP)将是一个新的发展。建议的优化例程将为功率半导体封装设计提供新的功能(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)。以及生产线上的最优过程控制。

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.