SURF Program Research Opportunities in Boulder, Colorado

HOST LABORATORIES

• Communications Technology Laboratory (CTL)
• Information Technology Laboratory (ITL)
• Material Measurement Laboratory (MML)
• Physical Measurement Laboratory (PML)

2026 - ReSEARCH OPPOrtunities

Communications Technology Laboratory (CTL)

RF Technology Division (Div 672)

672-1 Laser Interferometer Measurements of Piezoelectric Materials
Angela Stelson, angela.stelson [at] nist.gov (angela[dot]stelson[at]nist[dot]gov) & Robert Lirette, robert.lirette [at] nist.gov (robert[dot]lirette[at]nist[dot]gov)
(CHIPS) Piezoelectric materials, such as lithium niobate and aluminum nitride, are widely used in today’s communications devices. When designing and testing these devices, precise knowledge of the properties of these materials is essential to industry. At NIST, we are developing new techniques for measuring the piezoelectric and elastic constants of materials using non-contact laser interferometer methods. The selected student will work with their mentor in designing and performing related experiments to measure piezoelectric materials. They will gain hands on experience designing and performing experiments in a professional laboratory setting. The student will be trained on laser safety and general laboratory practices prior to performing any experiments and PPE will be provided as needed.

REQUIRED SKILLS: Engineering or physics related background
PREFERRED SKILLS: Programming experience in MATLAB or Python

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Information Technology Laboratory (ITL)

Applied & Computational Mathematics Division (Div 771)

Example 2025 Opportunity: 771-1 Geometric Interpretations for Pattern Recognition in Images
Zach Grey, zachary.grey [at] nist.gov (zachary[dot]grey[at]nist[dot]gov)
(CHIPS) Imaging is fundamental to human interpretation and measurement. When scientists and engineers begin to study an object or environment, they often begin with a picture/image. And, in that image, they often need to extract and compute statistics of coherent patterns or structures to compare and contrast pairs of pictures. We'll be working in-person with scientific computing and geometric methods applied to broad computer vision challenges such as solar ultraviolet imaging (SUVI) of the sun, electron backscatter diffraction (EBSD) of materials like lithium-ion batteries and steel, as well as x-ray computed tomography (xCT) of silicon chip packages. The position requires a candidate curious to explore some or all of the following topics in applied mathematics: (i) image segmentation and morphology, (ii) abstractions of calculus over curves and surfaces, (iii) topological data analysis, and (iv) implementations/comparisons with generative models. Familiarity with linear algebra and computational geometry is very helpful. Exceptional candidates will also have some experience with introductory real analysis and algebraic topology. Programming experience in Python/Matlab is essential. [In-person opportunity]

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Material Measurement Laboratory (MML)

Applied Chemicals and Materials Division (Div 647)

647-1 Finite Element Analysis of a Thermal Conductivity Acoustic Resonator
Karim Al-Barghouti, karim.al-barghouti [at] nist.gov (karim[dot]al-barghouti[at]nist[dot]gov) & Mark McLinden, mark.mclinden [at] nist.gov (mark[dot]mclinden[at]nist[dot]gov)
(CHIPS) The project aims on developing a novel thermal conductivity acoustic resonator for the characterization of semiconductor process gases. The student will learn and apply advanced FEA techniques to guide the design of the resonator and its response function. Simulations and theory will be used to develop a working model for extracting the thermal conductivity of gases at high pressures and temperatures. The student will also have access to existing acoustic resonators to help in understanding and developing the necessary model for the thermal conductivity resonator. The in-lab portion of the project will require extensive safety training and familiarization with standard operating procedures. The student will have access to a high-performance computing cluster.

REQUIRED SKILLS: Thermodynamics, Transport Phenomena, coding
PREFERRED SKILLS: FEA simulations, electronics, HPC, numerical methods

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Physical Measurement Laboratory (PML)

Applied Physics Division (Div 686)

686-1 Simulation and Measurement of Advanced Spintronic Devices and Materials
Matthew Pufall, matthew.pufall [at] nist.gov (matthew[dot]pufall[at]nist[dot]gov) & Charles Swindells, charles.swindells [at] nist.gov (charles[dot]swindells[at]nist[dot]gov) 
(CHIPS) Magnetic random-access memory (MRAM) is an emerging spintronic technology slated to take the place of FLASH in many future applications. MRAM uses ultrathin (<1.5 nm) magnetic layers to store information and spin currents to write it. At these length scales, bulk values for critical parameters are not accurate, so we are developing a combination of advanced measurements and simulations to determine parameters necessary for MRAM development. In this project the student will learn advanced magnetic characterization methods (both optical and inductive) and atomistic magnetic simulation methods (employing NIST high performance computing resources) we are developing to determine these critical parameters. The project focus (experiment or sim) is flexible, depending on the student’s interests.

REQUIRED SKILLS: Basic familiarity with Python, Introductory E&M coursework
PREFERRED SKILLS: Introductory solid state physics coursework. Laboratory coursework (experimental safety, setup). Abiding and tenacious curiosity.

Quantum Sensors Division (Div 687)

687-1 Improving 3D X-ray Imaging of Integrated Circuits using a Scanning Electron Microscope-based Computed Tomography Tool
Jordan Fonseca, jordan.fonseca [at] nist.gov (jordan[dot]fonseca[at]nist[dot]gov) & Nathan Nakamura, nathan.nakamura [at] nist.gov (nathan[dot]nakamura[at]nist[dot]gov) 
(CHIPS) X-ray computed tomography (xCT) is a powerful method for the nondestructive inspection of opaque objects, providing 3D images of their internal structure. At the nanoscale, this characterization is useful in the semiconductor industry to analyze circuit failure points and to understand device performance. However, modern nanoelectronics contain features too small and complex to be imaged by current commercial instruments. The Quantum Sensors Division at NIST has developed a prototype nano-xCT instrument and demonstrated 3D reconstruction of integrated circuits with 160 nm spatial resolution. The next phase of this research will improve the instrument’s spatial resolution, scanning speed, and ability to distinguish between metals in a circuit. As a SURF student, you will work with NIST physicists to operate and optimize a newly acquired scanning electron microscope (SEM) and commercial x-ray detector to perform nano-xCT on integrated circuits. You will determine how changing a wide variety of experimental parameters on the instrument impacts image quality and measurement time. You will learn general x-ray CT principles, data analysis and visualization in Python, and techniques for quantification and analysis of scientific images. You should have some familiarity with the basics of computer programming (e.g. for loops, data structures, use of functions), but experience in Python is not a prerequisite. You should be interested in learning more about the science of 3D x-ray imaging, semiconductor metrology, and contributing to applied physics research. Laboratory work will require that you complete required safety trainings and adhere to NIST safety guidelines, including training and directives as communicated by your mentors. You will work with lab equipment in a safe, approved configuration under supervision from your mentor.

REQUIRED SKILLS: Interested in learning more about the science of 3D x-ray imaging, semiconductor metrology, and contributing to applied physics research.
PREFERRED SKILLS: Some familiarity with the basics of computer programming (e.g. for loops, data structures, use of functions). Some familiarity with Python will be helpful, but experience in Python is not a prerequisite. 

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