Nanoelectronics Modeling Tools with NEMO5

Professor Gerhard Klimeck, Electrical and Computer Engineering

Faculty Advisor: Gerhard Klimeck

Project Description: nanoHUB is the home of an online community for scientific education and research related to nanotechnology. The iNEMO group, headed by Gerhard Klimeck, conducts research on nanoelectronics modeling. This includes a variety of areas such as next-generation transistor designs and materials, quantum computing, and advanced numerical algorithms. The cornerstone of this research is NEMO5, a multiphysics, multiscale software package developed by the iNEMO group. A core goal is to bring that nanoelectronics modeling capability to users' machines via easy-to-use nanoHUB web-based tools based on the NEMO5 engine. More information about the group is on the website (https://engineering.purdue.edu/gekcogrp/software-projects/nemo5/).

There are several projects available to SURF students including Quantum Dot Lab, 1D Heterostructure Tool, and Resonant Tunneling Diode Simulation with NEGF. Two other tools are in the early stages of development. The Quantum LED Modeling Tool will model light emitting diodes with advanced physics and the Configuration Interaction Tool will study fundamental properties of quantum computing (http://absimage.aps.org/image/MAR15/MWS_MAR15-2014-006599.pdf ).

Suggested Majors:  Electrical Engineering, Computer Engineering, Physics, or similar.

Required Skills:  Students should have a background in at least one of the following: materials, numerical algorithms, semiconductor device physics, optoelectronics or a related area. They must have a desire to bring a viable product to fruition which requires project and time management and attention to detail. Students will have the opportunity to learn about and use high-performance computing systems. No specific programming language experience is required, but students should have some experience coding and more importantly, a desire to learn and conduct software development. 

Additional Desired Skills, Courses Completed: Students will work under the supervision of a graduate student mentor but need to be able to work independently and problem-solve on their own.

Predictive Materials Simulation

Professor Alejandro Strachan, Materials Engineering

Faculty Advisor: Alejandro Strachan

Project Description: Our research focuses on the development of predictive atomistic and molecular simulation methodologies to describe materials from first principles, their application to problems of technological importance and quantification of associated uncertainties. Application areas of interest include: electronic, thermal and mechanical properties of nano- and micro-electromechanical systems and electronic and energy conversion devices; thermo-mechanical response of polymer composites and molecular solids; and physics and chemistry of active materials including shape memory and energetic materials.

Performance of novel III-V thin-film photovoltaic devices

Professor Peter Bermel, Electrical and Computer Engineering

Faculty Advisor: Peter Bermel

Project Description: Sunlight is a renewable and abundant resource that can be harvested with photovoltaic modules. To make photovoltaics more competitive with other energy technologies, there is an ongoing challenge to develop improved thin-film materials. In particular, materials made from the 3^rd and 5^th columns of the periodic table have been demonstrated to offer the best performance. Lower cost fabrication methods have recently been devised, must be further investigated to achieve high device and module-level performance. In our nanoHUB SURF research, we will construct a web-enabled simulation tool to accurately capture the unique performance associated with using novel III-V materials. We will also make this capability available for use by a broad audience. Our tool will be hosted and run through nanoHUB.org - an open-access science gateway for cloud-based simulation tools and resources in nanoscale science and technology.

Suggested Majors: Electrical Engineering, Physics, Computer Engineering.

Required Skills: Familiarity with introductory mechanics, electromagnetism, ray optics, and the basics of scientific computing. 

Additional Desired Skills, Courses Completed: The ability and inclination to quickly learn a new scientific topic is desired. Knowledge of the drift-diffusion equations for current transport is a plus. For coding, Python and MATLAB/Octave are preferred languages. Working familiarity with Linux and shell scripts is also a plus.

Design optimization of a steel wire drawing process with quantified uncertainties in incoming material

Professor Ilias Bilionis, Mechanical Engineering

Faculty Advisor: Ilias Bilionis 

Project Description: The steel wire drawing process is very common and important in the steel industry as the quality of the extruded wire determines the eventual quality of the final product. The aim in this research is to optimize multiple properties (objectives) of the outgoing wire and the system in general, keeping in consideration the uncertainties and the various sources of noise that contribute to the uncertainty associated with the objectives. We use a robust modified Efficient Global optimization (EGO) methodology to generate a number of Pareto optimal solutions.

Tool:

You will create a tool that generates a number of optimal solutions that lie on a Pareto frontier for a multi-objective optimization problem under uncertainty.

The multi pass wire drawing process has been modelled using a Finite element method (FEM) code which serves as a simulator of the various output values and the constraints representing the state of wire at each pass. The quantities of interest (QOI) to be optimized are physical and micro mechanical quantities like the power consumed, ultimate tensile strength etc.

The tool on nanohub will allow the user to enter the names of the QOIs that need to be optimized and the constraints that need to be satisfied. In this problem, the design variables are the reduction ratios and the angles of the dies at different passes. The methodology described above will build individual surrogate models for the different objectives and sequentially generate optimal values of the design variables using an Information acquisition function (IAF). The user can specify the maximum number of iterations or the tolerance on the improvement desired, depending on the budget (computational resources) that he has, to provide a stopping criterion for the optimization tool. The end result would be a bunch of points in the objective space lying on the Pareto frontier of the multi-objective optimization problem and the corresponding values of the optimal designs.

Required Skills:

• Mathematical Background (Calculus, Linear Algebra)
• Familiarity with basics of a programming language (Python or C)
• Basic knowledge of Probability theory

Additional Desired Skills, Courses Completed: 

• Basics of programming
• Design Optimization  course
• Linear algebra /Calculus
• Basic Probability theory

Optimization under Uncertainty Tool for Modeling Porous Lithium-Ion Batteries

Professor Guang Lin, Mechanical Engineering and Mathematics

Faculty Advisor: Guang Lin

Project Description: High-capacity battery is critical to our daily life. However, during the manufacturing process, electrode parameters such as cathode thickness, the volume fraction of electrolyte in positive electrode and radius of negative particles are subject to uncertainty. Such uncertainty may have a dramatic impact on the performance of the battery. To optimize the battery’s performance, we need to develop an efficient optimization framework, considering the uncertainty due to electrode parameters on the performance of the battery through porous lithium-ion battery computer simulations. In this project, the SURF student will investigate how to design a optimization under uncertainty framework, which includes to develop an uncertain parameter distribution input interface, a optimization under uncertainty interface and a visualization of the optimized battery performance interface.

In this project, the SURF student will learn how to run porous lithium-ion battery simulator, how to write python interface to run the simulator, and how to perform optimization under uncertainty, and visualize the optimized simulation results. The SURF student will work with a PhD student.

Suggested Majors: Material, Electrical Engineering, Math, Computer Science, Mechanical Engineering

Required Skills: Python, Matlab, basic probability and statistics.

Additional Desired Skills, Courses Completed: Fortran.

Uncertainty Quantification Visualization Tool for Modeling Porous Lithium-Ion Batteries

Professor Edwin Garcia, Materials Engineering

Faculty Advisor: Guang Lin,  R. Edwin García

Project Description: High-capacity battery is critical to our daily life. However, during the manufacturing process, electrode parameters such as cathode thickness, the volume fraction of electrolyte in positive electrode and radius of negative particles are subject to uncertainty. Such uncertainty may have a dramatic impact on the performance of the battery. To optimize the battery’s performance, it is critical to quantify the uncertainty due to electrode parameters on the performance of the battery through porous lithium-ion battery computer simulations. In this project, the SURF student will investigate how to design an uncertain parameter distribution input interface, a simulation interface and a uncertainty quantification visualization interface.

In this project, the SURF student will learn how to run porous lithium-ion battery simulator, how to write python interface to run the simulator, and how to compute the statistics of the simulation results, and visualize the uncertainty. The SURF student will work with a PhD student.

Suggested Majors: Material, Electrical Engineering, Math, Computer Science, Mechanical Engineering.

Required Skills: Python, Matlab, basic probability and statistics.

Additional Desired Skills, Courses Completed: Fortran.

Microstructure evolution during powder compaction

Professor Marcial Gonzalez, Mechanical Engineering

Faculty Advisor: Marcial Gonzalez

Project Description: 

Overall goal: The goal of this project is to develop new, and improve current, capabilities for modeling, simulation and prediction of microstructure formation and evolution during the compaction of powders.

Motivation: Compaction of powders is a manufacturing process used in many industries. It consists of the synthesis of loose powders into solid bodies. Many materials need to be made (or can only be made) in the form of small particles. The final products made of these materials, however, need to be of macroscopic size. The performance of these final products is directly related to their microstructural features, thus the fundamental understanding of the compaction process becomes of paramount importance. Powder compaction is used in the pharmaceutical industry when the active pharmaceutical ingredient is crystallized in the form of micro- or nano-particles to improve in vivo bioavailability. Nano-powders are pressed into solids to achieve unique thermo-electric and mechanical properties needed for electronic, energy and aerospace applications.

Description of the project: The specific goals of the project are to expand the current capabilities of the nanoHUB tool Powder Compaction by: (i) modeling compacted products of different shapes and sizes, (ii) accounting for powder beds with different particle size distributions.

Suggested Majors: Mechanical Engineering, Materials Engineering, Chemical Engineering, Computer Science.

Required Skills:

• Understanding some basics about materials mechanical properties
• Basic coding experience in MATLAB.

Additional Desired Skills, Courses Completed: A strong desire to develop additional skills and knowledge.

Developing a universal front-end for 2D photonic structures: meshed solvers

Professor Alexander Kildishev, Electrical and Computer Engineering

Faculty Advisor: Alexander Kildishev

Project Description: We are a computational nanophotonics team hiring undergrad developers for designing a sleek minimalist-style smart front-end for our novel 2D full-wave meshed solvers. The perspective candidates should be able to provide our global nanoHUB audience with the best user interface experience. We have already staged several QWT-based photonics tools at nanoHUB, e.g, https://nanohub.org/resources/sha2d; https://nanohub.org/resources/photonicsrt, which have been developed through SURF.

Later we have expanded our GUI development palette by using Matlab GUIDE in a series of new tools http://nanohub.org/resources/testgain0d, https://nanohub.org/resources/photonicspos, and https://nanohub.org/resources/photonicvasefit.

Hence, we are inviting motivated and smart GUI Developers, who are ready to expand their coding skills with our highly specialized team. If you recognize yourself in this description, join us!

Suggested Majors: CS, ECE.

Required Skills:

• Good programming skills in Matlab GUIDE & Java or/and QWT/C++
• Should love to design and code ambitious minimalist-style smart front-ends!

Additional Desired Skills, Courses Completed: No special prerequisites are necessary, yet in-depth knowledge of the Matlab GUIDE & Java or QWT/C++ is required.

Metal/dielectric thermal interfacial transport considering cross-interface electron-phonon coupling

Professor Xiulin Ruan, Mechanical Engineering 

Faculty Advisor: Xiulin Ruan

Project Description:  In this project, the undergraduate student will assist a PhD student to deploy a tool for simulating heat transfer across metal-dielectric interface. The conventional two-temperature equations for electron-phonon coupled thermal transport across metal/nonmetal interfaces have been modified to include the possible coupling between metal electrons with substrate phonons. Our previous two-temperature molecular dynamics (TTMD) approach has been extended to solve these equations numerically at the atomic scale, and the method is demonstrated using Cu/Si interface as an example. Based on the temperature profiles from TT-MD, a new mixed series-parallel thermal circuit can be constructed. This simulation tool will also be deployed to nanoHUB.

Suggested Majors: Mechanical Engineering, Physics, Electrical Engineering, Materials Science.

Required Skills: Heat transfer, programming 

Thermophotovoltaic simulations with experimentally realistic considerations

Professor Peter Bermel, Electrical and Computer Engineering

Faculty Advisor: Peter Bermel

Project Description: Waste heat is an abundant resource, recoverable from the environment via the emerging technology of thermophotovoltaics (TPV). In TPV, a hot emitter illuminates a low-bandgap solar cell to generate electricity. Making this technology relevant to current energy challenges requires designing new types of experimentally-relevant structures for high performance. In our nanoHUB SURF research, we will construct a GUI-based simulation tool to capture the details of the optical, thermal, and electrical processes driving TPV. We will use this to investigate new types of selective emitters and PV cooling technologies for improved performance, and make this available for use by a broad audience. Our tool will be hosted and run through nanoHUB.org - an open-access science gateway for cloud-based simulation tools and resources in nanoscale science and technology.

Suggested Majors: Electrical Engineering, Physics, Computer Engineering.

Required Skills: Familiarity with introductory mechanics, electromagnetism, and the basics of scientific computing. 

Additional Desired Skills, Courses Completed: The ability and inclination to quickly learn a new scientific topic is desired. Knowledge of the drift-diffusion and heat diffusion equations for current and heat transport is a plus. For coding, Python and MATLAB/Octave are preferred languages. Working familiarity with Linux and shell scripts is also a plus.

Web-based simulation of granular materials and nanoparticle self-assembly

Professor Timothy Fisher, Mechanical Engineering

Faculty Advisor: Timothy Fisher

Project Description: Understanding the physics of manufacturing heterogenous materials via the popular processes of granular jamming, colloidal gelation and nanoparticle self (or directed) assembly is crucial to designing tailored materials for functional applications such as energy devices (e.g., batteries, thermoelectrics, photovoltaics), bio-sensing and electrocatalysis. At sub-micron length scales, the interparticle interactions are complex and dominated by conservative (van der Waals and electrostatic) and entropic (polymer-mediated or depletion dictated by particle shape) forces. This project seeks to develop a consolidated, web-based suite of computational tools (to be hosted on nanoHUB) to simulate the aforementioned processes. We employ optimization routines to explore the free energy hyperspace and predict stable microstructures along with detailed geometric (incl. visual) and mechanical characterization. The availability of such tools (for rapid prototyping of these materials) through open-source hosting will be of great benefit to researchers (experimental and computational) working in this field.

Expected Student Contributions: This project is primarily computational and will involve: a) object-oriented development (and extension) of existing prototype code for simulations, b) hosting the code on nanoHUB for a web-based interface, and c) code parallelization.

Desired Qualifications: Interest in mechanics and soft matter physics. Strong programming skills. Knowledge of MATLAB, C/C++ is required. Prior experience with object-oriented programming is highly desired.