Prof. Carl V Thompson

Stavros V and Matoula S Salapatas Professor of Materials Science and Engineering
Co-Director, Materials Research Laboratory (MRL)

Primary DLC

Department of Materials Science and Engineering

MIT Room: 13-5069


Ryan Kendall

Areas of Interest and Expertise

Reliability of Si-Based Integrated Circuits
Reliability of GaN-Based Devices; HEMTs and LEDs
Origin and Cdontrol of Mechanical Stress in Thin Films
Structure Evolution During Deposition and Processing of Thin Films
Thermal Stability of Thin Films and Nanostructures
Integrated Solid-State Supercapacitors
Thin Film Microbatteries
Rechargeable Li-Air Batteries

Research Summary

Professor Thompson and his students carry out research on thin films and nanostructures for use in micro- and nano-systems, especially electronic, electromechanical systems and electrochemical systems. One area of special interest is structure evolution during film formation and during post-patterning processing. For these studies, in-situ stress and structure characterization are combined to study kinetic processes that affect the properties of films and surfaces, both during and after deposition. Probe-based and electron microscopy are also used to characterize structure evolution in continuous and patterned films, as a function of thermal, mechanical, and electrical processes. Another major theme in Professor Thompson's research is development of techniques for organizing large systems of nano-scale materials, including carbon nanotubes, semiconductor and metallic nano-wires, and metallic nano-crystals. Applications of interest included sensing, energy storage/management, and water treatment.

Recent Work

  • Video


    November 4, 2020Conference Video Duration: 40:31
    Over the past several decades the iterative trial-and-error approach to alloy design has become dramatically ‘digitally enhanced’.  Physically-motivated computational models that incorporate thermodynamics, kinetics, and processing pathways can substantially narrow the search for optimum alloy compositions and configurations, while high-throughput experimental methods accelerate iteration. In advanced research areas where the controlling physics are not always known, computation can be augmented with data science and machine learning methods to span vast compositional spaces where few experiments exist. This talk will highlight projects of MIT faculty contributing to the digital transformation of the innovative ‘front-end’ of the metals industry—the design and reduction-to-practice of new alloys.


    October 14, 2020Conference Video Duration: 16:15
    MIT’s Interdisciplinary Materials Research Laboratory