Device Packaging 2025
Date & Time - March 3rd-6th, 2025
Location - Phoenix, Arizona
Schrödinger is excited to be participating in the Device Packaging 2025 conference taking place on March 3rd – 6th in Phoenix, Arizona. Join our poster and collaborated talk with Samsung. Stop by booth #704 to speak with us.
MAR 5 | 5:30 PM Poster: Materials innovation for advanced electronic packaging using digital chemistry
Speaker:
Atif Afzal, Principal Scientist II, Schrödinger
Abstract:
The push for ever-improving characteristics of electronic devices demands packaging materials with superior thermal stability, mechanical strength, water repellency, and interfacial properties. Traditional material selection methods, often reliant on extensive empirical testing, are time-consuming and costly, limiting the ability for researchers to push beyond what they already know. To address these challenges, we propose a new approach that integrates physics-based modeling with machine learning (ML) to accurately model and predict the properties of advanced materials for electronic packaging. Our physics-based modeling, molecular dynamics (MD) simulations, offer detailed atomistic insights into material behavior under various conditions, providing essential data on thermal properties, mechanical resilience, adhesion, and more. To accelerate the material evaluation process and to navigate new chemical domains more efficiently, we integrate ML in our workflows. By training ML models using both experiment and simulation data, we can rapidly predict the properties of new materials, enabling efficient screening and selection.
We demonstrate the efficacy of this approach through a case study focused on designing copolymers with targeted properties. Our integrated MD-ML framework allows us to quickly identify polymers that meet specific performance criteria, such as enhanced glass transition and superior dielectric properties, while significantly reducing the time and resources required for material discovery. This work highlights the transformative potential of combining physics-based simulations with machine learning in the field of electronic packaging. By streamlining the material development process, our approach not only accelerates innovation but also enables the creation of materials that meet the stringent demands of next-generation electronic devices.
MAR 6 | 10:30 AM Talk: Material property simulation for advanced packaging
Speaker:
Yan Li, Samsung
Abstract:
Advanced packaging allows chiplet integration and maximizes device performance with faster product development cycle, lower cost, and higher yield. As the package size becomes bigger and the device is getting more complicated, there is growing motivation to employ manufacturing process simulation, Artificial Intelligence (AI) assisted process optimization, yield and reliability prediction, rather than conventional methods, to ramp the yield and to ensure the reliability of a new product. The key for an accurate process simulation model is to input precise material properties, such as modulus, Coefficient of Thermal Expansion (CTE), dielectric constant, glass transition temperature, etc., which could change non-linearly with temperature, moisture, as well as other environmental factors and process conditions. Molecular modeling and molecular dynamics can provide insights into post chemical reactions or physical transformations via atomic and molecular simulations
Lithography Techniques for Redistribution Layer (RDL) fabrication are the foundation of Advanced Packaging techniques, such as Fan Out Wafer Level Packaging (FOWLP), Fan Out Panel Level Packaging (FOPLP), 2.5D, 3D, and 3.5D packaging with RDL interposers. The continuous scaling-down of critical dimensions (CDs) in advanced packages, including via diameters, routing line and space (L/S), to a few microns, or submicron level, as well as the increasing number of RDL layers at panel scale pose significant challenges in RDL lithography techniques. For example, the Photo Imageable Dielectric (PID) or other build-up dielectric materials used in multilayer RDL fabrication are polymers, having low Young’s modulus, high CTE, and big volume shrinkage after curing. These material properties could cause fabrication process induced warpage and surface topography deformations, such as non-planarity, roughness, contamination, defects, and dimensional variations, which could potentially lead to massive yield loss when forming fine features during the multilayer RDL patterning.
This paper presents material simulation methodologies based on quantum mechanics (QM), molecular dynamics (MD), and Machine Learning (ML), which are adopted to predict the material properties of a PID material, including glass transition temperature (Tg), CTE, mechanical properties, dielectric properties, as well as volume shrinkage after curing. Comparison between the simulation results and the experimental data is performed to validate the methodology. Similar methodology could be used to predict material properties of other organic packaging materials, which is crucial for building up accurate process, yield, and reliability simulation or digital twin of advanced packaging.