Spatially Resolved Nanothermal Transport Of Multilayer And Phononic Structures Using Scanning Thermal Microscopy

Spatially Resolved Nanothermal Transport of Multilayer and Phononic Structures Using Scanning Thermal Microscopy

Quantitative Mapping of Nanothermal Transport via Scanning Thermal Microscopy

The thesis tackles one of the most difficult problems of modern nanoscale science and technology - exploring what governs thermal phenomena at the nanoscale, how to measure the temperatures in devices just a few atoms across, and how to manage heat transport on these length scales. Nanoscale heat generated in microprocessor components of only a few tens of nanometres across cannot be effectively fed away, thus stalling the famous Moore's law of increasing computer speed, valid now for more than a decade. In this thesis, Jean Spièce develops a novel comprehensive experimental and analytical framework for high precision measurement of heat flows at the nanoscale using advanced scanning thermal microscopy (SThM) operating in ambient and vacuum environment, and reports the world’s first operation of cryogenic SThM. He applies the methodology described in the thesis to novel carbon-nanotube-based effective heat conductors, uncovers new phenomena of thermal transport in two- dimensional (2D) materials such as graphene and boron nitride, thereby discovering an entirely new paradigm of thermoelectric cooling and energy production using geometrical modification of 2D materials.

Modeling and Uncertainty Quantification of Non-contact Scanning Thermal Microscopy

Since its introduction, Scanning Thermal Microscopy (SThM) has been widely used to measure surface temperature and thermal properties of nano-scale materials and structures with high spatial resolution. However, discrepancy exits between the temperature read by the SThM probe and the actual temperature of sample measured. In addition, the temperature of the measured sample can be affected by the presence of the SThM probe. In this thesis work, we used Ansys Fluent to develop a SThM model to establish calibration between the temperature read by the SThM probe and the actual temperature of measurement. The effects of the probe on the temperature of sample is also quantified. We use Bayesian inference to calibrate the unknown thermal conductivities of the polymer (substrate). This model is validated by comparing its predictions with experiment observations. We also quantify the uncertainties in the Quantity of Interest (QoI), the probe tip temperature, due to the uncertainty in the simulation input parameters. This is accomplished by using a generalized polynomial chaos (gPC) formalism. A response surface relating the QoI to model inputs is constructed through stochastic collocation. A Smolyak sparse grid is used to reduce the computation expense. The response surface is sampled based on the PDFs of the input parameters to obtain the PDF of the QoI. We find the uncertainty in the cross-plane thermal conductivity of the liquid polymer and the diameter of the probe tip have large contributions to the overall uncertainty in the QoI.