Periodic Reporting for period 1 - MIRELAI (MIcroelectronics RELiability driven by Artificial Intelligence)
Reporting period: 2022-10-01 to 2024-09-30
The MIRELAI industrial doctorate programme trains the next generation of engineers and scientists in the area of reliable and repairable Electronic Components and Systems (ECS) developed within a trustworthy European value chain.
The European microelectronics (ME) industry has a direct impact on approximately 20% of the European GDP and employs over 250,000 people, with more than 64,000 job vacancies. The main technological challenges are 1) to increase both the reliability of the manufactured ECS and sustainability to meet the requirements of the new EU directive Right to Repair, and 2) to reduce the product verification efforts (70% of total product development time) that represent a substantial burden on costs and resources. To compete with Asia and North America, the European ME industry is in critical need of cross-discipline experts in electronic manufacturing and digital innovations: software, data and artificial intelligence.
Our unique industry-academia partnership of 8 industries, 4 SMEs and 9 research organisations and academic institutions from 7 European countries has all the expertise, experience and capacity along the electronics system value chain to deliver this ambitious research and training programme. Shared hosting and joint supervision by industry and academia of each of the 13 doctoral candidates ensures optimal knowledge transfer. Together, we will pave the way for sustainable, repairable and energy-efficient electronic system designs and resource-friendly smart electronics applications.
The European microelectronics (ME) industry has a direct impact on approximately 20% of the European GDP and employs over 250,000 people, with more than 64,000 job vacancies. The main technological challenges are 1) to increase both the reliability of the manufactured ECS and sustainability to meet the requirements of the new EU directive Right to Repair, and 2) to reduce the product verification efforts (70% of total product development time) that represent a substantial burden on costs and resources. To compete with Asia and North America, the European ME industry is in critical need of cross-discipline experts in electronic manufacturing and digital innovations: software, data and artificial intelligence.
Our unique industry-academia partnership of 8 industries, 4 SMEs and 9 research organisations and academic institutions from 7 European countries has all the expertise, experience and capacity along the electronics system value chain to deliver this ambitious research and training programme. Shared hosting and joint supervision by industry and academia of each of the 13 doctoral candidates ensures optimal knowledge transfer. Together, we will pave the way for sustainable, repairable and energy-efficient electronic system designs and resource-friendly smart electronics applications.
An innovative training programme has been implemented to equip students with expertise in materials physics, numerical simulation, and machine learning – an essential but largely unprecedented combination for the microelectronics industry. The programme involves leading European industrial and academic experts in the field of ME, aiming to enhance the reliability and durability of microelectronics as industry demands grow. The key highlights of the training programme so far include individual and network-wide training sessions, featuring close supervision by at least two supervisors with academic and industrial expertise. All Doctoral Candidates (DCs) are enrolled in a local PhD programme. The training activities also include two on-site training weeks, a machine learning workshop, and quarterly webinars with expert talks.
The scientific achievements of the ongoing programme, MIRELAI, can be clustered into three categories which also represent the three scientific work packages (WPs).
Within WP4 – Physics of degradation – the individual DCs developed a thorough understanding of the problem-specific relevant failure modes and degradation behaviour. The degradation behaviour and potential failure of microelectronic systems is studied by the DCs on different levels. While some theses focus on defined features of Printed Circuit Board Assemblies (PCBAs), namely microvias, solder joints and Plated Through Holes (PTH), selected theses focus on system-level effects. On the feature level, a result example is the structured overview of potential failure modes for microvias based on literature data, as well as on in-house experience from the industry partner. Another example is a new analysis option for subsurface defects based on Lock-In Thermography (LIT). On the system level for example the effect of material, geometry and conducting path design variations on the warpage behaviour has been studied and summarised. Also, a new accelerated physics of degradation testing method on the system level is developed. The power cycle test, acting as a reference has been installed, and the resulting loading conditions have been analysed in detail. They will now be used to define and establish an accelerated mechanical substitute test in the ongoing project.
Within WP5 – Multi-scale modelling – the DCs generated virtual representations of their problem statements. Digital twins have been set up on local and global levels. At a local level, for example, a finite element model was developed at the microstructure level for the detailed investigation of the degradation of solder joints. It models the grain-scale behaviour of solder joints which can be used to detect how effects such as crystal orientation or Euler angles influence the mechanical response of solder joints in component assemblies. On a global level again a finite element-based thermal analysis of a complete PCBA using Reduced Order Modelling (ROM) was introduced. It allowed us to effectively use virtual sensing to correct the model parameter uncertainties based on a Kalman filter approach. The effectiveness of the approach was showcased in a study of different die attachment solutions. The model results of the individual problem statements are used to gain a further understanding of the failure modes and to generate training data considering uncertainties and parameter variations for potential metamodels.
WP6 – AI-based reliability – built upon the results of WP4 and WP5. On the one hand, specific identified failure modes and their physics are analysed testing the potential of Physics-Informed Neural Networks (PINN). One example is the non-linear buckling of a needle which has been examined using PINN. On the other hand, generated training data is used to generate metamodels to screen a wide parameter space. For example, an artificial neural network could be trained to predict the warpage of substrates for a wide range of material and geometry parameter variations. In a simplified application, the approach has already demonstrated its ability to optimise substrate design with the aim of minimising warpage.
The scientific achievements of the ongoing programme, MIRELAI, can be clustered into three categories which also represent the three scientific work packages (WPs).
Within WP4 – Physics of degradation – the individual DCs developed a thorough understanding of the problem-specific relevant failure modes and degradation behaviour. The degradation behaviour and potential failure of microelectronic systems is studied by the DCs on different levels. While some theses focus on defined features of Printed Circuit Board Assemblies (PCBAs), namely microvias, solder joints and Plated Through Holes (PTH), selected theses focus on system-level effects. On the feature level, a result example is the structured overview of potential failure modes for microvias based on literature data, as well as on in-house experience from the industry partner. Another example is a new analysis option for subsurface defects based on Lock-In Thermography (LIT). On the system level for example the effect of material, geometry and conducting path design variations on the warpage behaviour has been studied and summarised. Also, a new accelerated physics of degradation testing method on the system level is developed. The power cycle test, acting as a reference has been installed, and the resulting loading conditions have been analysed in detail. They will now be used to define and establish an accelerated mechanical substitute test in the ongoing project.
Within WP5 – Multi-scale modelling – the DCs generated virtual representations of their problem statements. Digital twins have been set up on local and global levels. At a local level, for example, a finite element model was developed at the microstructure level for the detailed investigation of the degradation of solder joints. It models the grain-scale behaviour of solder joints which can be used to detect how effects such as crystal orientation or Euler angles influence the mechanical response of solder joints in component assemblies. On a global level again a finite element-based thermal analysis of a complete PCBA using Reduced Order Modelling (ROM) was introduced. It allowed us to effectively use virtual sensing to correct the model parameter uncertainties based on a Kalman filter approach. The effectiveness of the approach was showcased in a study of different die attachment solutions. The model results of the individual problem statements are used to gain a further understanding of the failure modes and to generate training data considering uncertainties and parameter variations for potential metamodels.
WP6 – AI-based reliability – built upon the results of WP4 and WP5. On the one hand, specific identified failure modes and their physics are analysed testing the potential of Physics-Informed Neural Networks (PINN). One example is the non-linear buckling of a needle which has been examined using PINN. On the other hand, generated training data is used to generate metamodels to screen a wide parameter space. For example, an artificial neural network could be trained to predict the warpage of substrates for a wide range of material and geometry parameter variations. In a simplified application, the approach has already demonstrated its ability to optimise substrate design with the aim of minimising warpage.
Within MIRELAI the doctoral candidates have a unique opportunity to deepen their knowledge and experience in a future-oriented interdisciplinary field of research. By being training both in academic and industry settings, they will be enabled to have a major impact on how and to which extend microelectronics reliability can be improved to become a unique selling point (USP) within a European electronics-based systems value chain. Not only will they represent a strong network of experts within the European key players in the microelectronics ecosystem, but they will also leave an established path to gain the necessary knowledge in the relevant research areas. The programme has developed new workshops, lectures and methods that will be accessible to future generations of scientists in the field of reliability in microelectronics.