Closed Loop Digitalised Data Analytics and Analysis Platform (DAAP) for Intelligent Design and Manufacturing of Power Electronic Modules
Lead Research Organisation:
University of Greenwich
Department Name: Mathematical Sciences, FACH
Abstract
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.
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.
Publications
Hassan S
(2024)
Coupled thermal-mechanical analysis of power electronic modules with finite element method and parametric model order reduction
in Power Electronic Devices and Components
Rajaguru P
(2024)
Damage Mechanics-Based Failure Prediction of Wirebond in Power Electronic Module
in IEEE Access
Zhang X.
(2024)
IGBT Module DPT Efficiency Enhancement Via Multimodal Fusion Networks and Graph Convolution Networks
in IEEE Transactions on Industrial Electronics
Description | Real-time Virtual Prototypes for the Power Electronics Supply Chain |
Amount | £1,036,589 (GBP) |
Funding ID | EP/X024377/1 |
Organisation | Engineering and Physical Sciences Research Council (EPSRC) |
Sector | Public |
Country | United Kingdom |
Start | 06/2023 |
End | 03/2026 |
Title | High fidelity model of power electronic module to analyse impact of manufacturing uncertainties |
Description | This is a stage alpha finite element model developed in the project to investigate effects of manufacturing tolerances that influence the quality of wire bonded power electronic module and how these may propagate into subsequent reliability performance of the electronic device. Continuing model development in progress. Will be made available at a later stage of the project. |
Type Of Material | Computer model/algorithm |
Year Produced | 2023 |
Provided To Others? | No |
Impact | The model is developed to generate results within an approach that will also enable the creation of a metamodel. The metamodel will be computationally efficient and will be capable to perform sensitivity analysis on key manufacturing and product parameters, and will be part of the device datasheet. This will help manufactures of power electronic modules to gain understating of process parameters that need to be controlled, and also end users to deploy with confidence these devices in different applications that have different requirements. The full impacts will be achieved at the end of the project when all related developments within the project are completed. |
Description | DAAP module characterisation |
Organisation | University of Nottingham |
Country | United Kingdom |
Sector | Academic/University |
PI Contribution | Under this project, we engaged with these universities to access particular expertise and advanced metrology equipment. We have prepared and provided the specification requirements in relation to a power module characterisations which can provide datasets for the modelling methodologies researched in the project. |
Collaborator Contribution | Chatressiation datasets of devoices underpinning the modelling-related research in the project. |
Impact | Output creation is currently in progress. |
Start Year | 2023 |
Description | DAAP module characterisation |
Organisation | University of Sheffield |
Country | United Kingdom |
Sector | Academic/University |
PI Contribution | Under this project, we engaged with these universities to access particular expertise and advanced metrology equipment. We have prepared and provided the specification requirements in relation to a power module characterisations which can provide datasets for the modelling methodologies researched in the project. |
Collaborator Contribution | Chatressiation datasets of devoices underpinning the modelling-related research in the project. |
Impact | Output creation is currently in progress. |
Start Year | 2023 |
Description | Real time prototypes for PEM |
Organisation | University of Nottingham |
Country | United Kingdom |
Sector | Academic/University |
PI Contribution | This collaboration has shaped in the form of preparing joint project proposal between the University of Greenwich and the University of Nottingham. The project has been successful. My contribution is as project Co-I where the main input is in developing new modelling technologies for prediction thermo-mechanical reliability (lifetime modelling) of power electronic modules. This has been substantially informed by this EPSRC project and the early research that has been taken place on related modelling capability development. |
Collaborator Contribution | University of Nottingham contributed with research proposal related top the electro-thermal modelling of PEM virtual prototypes, semiconductor modelling, and experimental testing and validation |
Impact | No outputs yet, partnership as a project to start in June 2023. The only outcome at present is the grant award for the proposed project. |
Start Year | 2022 |
Description | Project referenced along with an outline to a research audience abroad, through an invited talk at Technical University of Sofia, Bulgaria, March 2023 |
Form Of Engagement Activity | A talk or presentation |
Part Of Official Scheme? | No |
Geographic Reach | International |
Primary Audience | Other audiences |
Results and Impact | As part of an invited talk, outline of the project, its aims and objectives, and key technologies researched. Emphasis was on opportunities for collaborative Research in the stated areas. |
Year(s) Of Engagement Activity | 2023 |