Motivation and Objectives
Unique electrical, optical, and thermal properties of 2D layered materials make them promising for many technological applications. MoS2 has received significant scientific attention in last few years because of the unique features of its 2D layers such as high band gap (desired for many electronic and optical devices), absence of dangling bonds, and thermal stability up to 1000 oC1, 2. MoS2 field-effect-transistors (FETs) with very high electronic performance have been demonstrated already. There are multiple challenges, which need significant attention to achieve the ultimate performance limits of MoS2 FETs in a scalable fashion such as (i) Development of synthesis methods for high-quality MoS2 monolayers compatible with large scale device fabrication - Statistical framework for rigorous quantification of the structure and its correlation with property can significantly help in devising methods for synthesis of high quality MoS2 layers, but such framework does not exist; (ii) Effective removal of heat, and operation of FETs below the threshold temperatures, which could be a critical challenge considering the low thermal conductivity of MoS2. Multi-scale modeling tools are required to understand the effect of MoS2 structure and surrounding material on its thermal behavior.
The objectives of this research are:
- to develop multi-scale modeling techniques for predicting thermal properties and analyzing energy transport at interfaces of MoS2
- to develop and employ data analytic techniques for establishing structure-thermal property linkages and quantify the uncertainties in property prediction by models
Multiscale Thermal Transport Model: A predictive model will be developed which will be able to estimate the thermal conductivity and interfacial thermal conductance at MoS2 interfaces with metallic/insulating structures using first principle without any fitting parameters. Metallic structures will be Au/Pt and insulating structures will be oxides, both of which are commonly used in nano-electronic devices and will have dominant effect on temperature distribution, thermal property and ultimate performance of the device. The research will develop a multi-scale computational framework, i.e., density functional theory will be used to predict structure and compute force constants to describe the interatomic interactions. These will be input to the atomistic models like non-equilibrium Green’s function and Boltzmann transport equations based model to compute thermal properties of MoS2 and it contacts considering the influences of defects and other structural features. Prof. Kumar’s expertise is in thermal transport analysis in nano- structures and devices using multi-scale models. He will guide this part of research.
Data Analytics for Structure-Property Linkages: n-point spatial correlations will be developed to represent structure of MoS2 layers using the inputs from existing experimental studies. The structure will be organized by increasing amounts of information at different scales (e.g., nanometers-to-micrometers). This representation of structure will offer many advantages in fast computation of structure measures/metrics, automated identification of salient structural features in large datasets, extraction of representative volume elements (RVEs) from an ensemble of datasets, reconstructions of structures from measured statistics and mining of high fidelity multi-scale structure-property (SP) evolution linkages from physics-based models3-7. RVEs will be built and correlated to computed thermal properties to make SP linkages and finally to identify the structure for optimal properties. Finally, the research will also quantify the uncertainty in the prediction of thermal properties from physics based models using statistical models. Prof. Kalidindi is expert in developing statistical techniques and data analytics methods for structure-property relationship and uncertainty quantification and will lead this part of the research.
Commercialization Opportunities: The multi-scale modeling tools for prediction of structure-thermal property linkage and uncertainty quantification in such predictions is not available in any commercial software. There will be high potential for development and commercialization of such software tools.
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