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

电力电子模块（PEMs）和更高级别的系统在可调速驱动器、统一电能质量校正、与可再生能源的公用事业接口、储能系统、电动或混合动力汽车以及更多的电动船舶/飞机中发挥着越来越重要的作用。电力电子技术为电力转换提供了紧凑、高效的解决方案，但在此类应用中部署电力电子模块面临着可靠性和安全运行的挑战。该项目旨在解决电力电子制造商和PEM最终用户持续面临的四个关键挑战：挑战1：没有在线和无损检测方法用于PEM封装质量和内部完整性评估（线键、模具附着和封装剂）嵌入生产线。挑战2：没有关于设计质量可靠性特征的全面PEM数据，没有图表化和测试数据集成和管理的流程，也没有数据建模和分析的流程。挑战3：缺乏准确评估PEM部署风险和生命周期管理的高级功能。挑战4：最终用户没有或只有有限的数据反馈给PEM设计人员/制造商，没有应用知情的设计和制造质量。该项目旨在为PEMs开发数字化数据分析和分析平台（DAAP）。以下是该项目中新颖且超越当前最先进的发展，可解决上述挑战：1)具有实时数据采集能力的无损检测（NDT）。一种使用LF-OCT成像的无损检测新技术将得到增强和优化，为单个PEMs提供高质量的数据。所提出的无损检测方法可以定量测量纳米级胶包焊线的力学变形。它可以捕获整个横截面图像，而无需任何机械扫描，提供与包装过程同步运行的新能力。2)使用人工智能和机器学习（ML）进行质量预测：将研究多种数据格式和来源的集成和使用，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、离线x射线和其他成像技术。通过支持ML和深度学习模型的开发，集成的数据将支持每个PEM的准确和自动化质量评估。建模能力将使包装质量评估基于设计和包装过程属性的综合集。3)可靠性预测。通过提出在可靠性预测中包含制造质量特征和设计属性，将推进目前最先进的PEMs设计可靠性和服役退化建模。通过分析制造和最终用户数据，这将增强知识和更准确的质量信息可靠性建模，并深入了解设计、质量和可靠性之间的关系。4)数据建模-优化能力集成。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。提出的优化程序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）和生产线上的最佳过程控制。

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.