Welcome to MiNDS Lab!

Our research focuses on investigation of energy transport and conversion in electronic materials and devices at different length scales. The MiNDS lab establishes multi-physics and mutli-scale computational and experimental framework to enhance the energy efficiency, performance and reliability of electronic devices and systems. At small scales we target on wide-bandgap devices, thin-film transistors, 2D materials based heat spreaders for on-chip hot spot-cooling, or materials/devices for extreme conditions. At large scales we target on cooling solutions for the efficient thermal management of electronic devices used in commercial or defense applications such as electric motors, data centers or forward operating bases.

    The goals of our lab are to develop principles and theories at small scales and translate them to large scales to engineer the system properties and performance. For example, we develop atomistic models to analyze electro-thermal transport in 1D and 2D nano-structures and their interfaces and develop meso-scale modeling techniques to analyze performance and reliability of devices made by these structures. We investigate the fundamental transport mechanism in a broad range of materials such as boron nitride, gallium-oxide, graphene, nanotubes, and polymers which are promising to revolutionize the performance and efficiency of next generation of micro-electronics, power-electronics, RF electronics, etc. We develop machine learning enabled multi-scale models that can be employed for rapid and accurate thermal transport analysis of 3D electronic packages, embedded thermo-electrics, electric motors, etc. We use ultra-fast thermo-reflectance imaging and frequency-domain thermo-reflectance (FDTR) techniques for high-fidelity thermal metrology of electronic devices and materials.

     MiNDs lab is directed by Dr. Satish Kumar. Sponsors of our lab include National Science Foundation (NSF), Office of Naval Research (ONR), Defense Advanced Research Projects Agency (DARPA), Semiconductor Research Corporation (SRC), Dept. of Energy (DOE), Oak Ridge National Lab (ORNL) and various Industries.

Convolutional Neural Network for Machine Learning Potential                                                                                                          
Electric Motor Thermal Management

Gallium Oxide for Power/RF Electronics