arXiv subjects

Prabudhya Roy Chowdhury

Publications and source records attributed to Prabudhya Roy Chowdhury.

2 recordsLinked to original sources

arXiv2026

A Multiscale Workflow for Thermal Analysis of 3DI Chip Stacks

Thermally aware design of 2.5D and 3D advanced packaging systems will require fast, accurate, and powerful thermal analysis of chiplets, stacks, and packages. These systems contain multiple materials with non-linear heat transfer properties and geometric feature sizes that span many orders of magnitude. The smallest heterostructures in the front and back ends of the line present significant thermal modeling and analysis challenges in isolation. Replicated millions or billions of times in a chiplet stack, these structures present a near insurmountable hurdle to meeting the speed and accuracy needed of analysis in the design process. Additionally, establishing precise parameter values for the materials in these systems, when size and temperature dependencies create significant deviations from bulk properties, further complicates the problem. To address these issues, we have developed a multiscale methodology that advances the current state of the field by enabling die-scale simulations that capture phenomena arising from the structural details of the BEOL metallization stack. Taking advantage of the large length-scale separation between the BEOL features and the die-level structures, we employ a hierarchical, multiscale, finite-element approach. This hierarchical method uses a standard finite element method (FEM) formulation on a die or package scale, using computational homogenization to obtain effective thermal conductivities in the BEOL. Referring to industry-standard layout and design files, we construct and solve a locally appropriate subscale FEM problem in a representative volume element (RVE) at every quadrature point in the macroscale FEM problem. To accomplish this, RVE models are automatically constructed, meshed, and used to compute homogenized, anisotropic, thermal conductivities from the relevant GDSII or OASIS.

cs.OH↗

arXiv2020

An iterative machine learning approach for discovering unexpected thermal conductivity enhancement in aperiodic superlattices

While machine learning (ML) has shown increasing effectiveness in optimizing materials properties under known physics, its application in challenging conventional wisdom and discovering new physics still remains challenging due to its interpolative nature. In this work, we demonstrate the potential of using ML for such applications by implementing an adaptive ML-accelerated search process that can discover unexpected lattice thermal conductivity ($κ_l$) enhancement instead of reduction in aperiodic superlattices (SLs) as compared to periodic superlattices. We use non-equilibrium molecular dynamics (NEMD) simulations for high-fidelity calculations of $κ_l$ for a small fraction of SLs in the search space, along with a convolutional neural network (CNN) which can rapidly predict $κ_l$ for a large number of structures. To ensure accurate prediction by the CNN for the target unknown structures, we iteratively identify aperiodic SLs containing structural features which lead to locally enhanced thermal transport, and include them as additional training data for the CNN in each iteration. As a result, our CNN can accurately predict the high $κ_l$ of aperiodic SLs that are absent from the initial training dataset, which allows us to identify the previously unseen exceptional structures. The identified RML structures exhibit increased coherent phonon contribution to thermal conductivity owing to the presence of closely spaced interfaces. Our work describes a general purpose machine learning approach for identifying low-probability-of-occurrence exceptional solutions within an extremely large subspace and discovering the underlying physics.

cond-mat.mes-hall↗