Post on 25-Oct-2020
Unclassified
RA3:Survivability and Response
Survivability Testing, Collection, and
Analysis
Name: Steve Pearton
Date: 8/27/2020
University of Florida, Penn State, University of Michigan, BYU,
Sandia National Labs, US Naval Research Lab
Unclassified2
Overview
• RA3: Survivability and Response / FA1: Survivability Testing FA2: Collection and Analysis,
FA3: RN Contamination, FA4: Shielding (delayed)
• IPP team: 3.1 Survivability Radiation Testing and Modeling of Complex Systems on a Chip
(SOC). Wirthlin/Goeders (BYU), Haque (PSU), Black (SNL) RA3-FA1,FA2
3.2 Survivability & Response: From SOC to Single Transistor. Haque, Wolfe
(PSU), Black (SNL),Wirthlin/Goeders (BYU) RA3-FA1,FA2
3.3 Electrical and Structural Characterization of Radiation Damage in Ultra-Wide
Bandgap Semiconductors. Pearton, Ren and Hartig (UF), Haque (PSU), Khachatrian (NRL)
RA3-FA1.
3.5 Long-range Radioactive Contamination Detection and Tracking using Intense
Laser Filaments. Jovanovic, Hammig (UM) Hartig (UF) RA3-FA2,FA3.
3.7. Designed Selectivity in Zeolites for Radionuclide Gas Adsorption and
Detection. Hartig (UF), Nino (UF), Nenoff (SNL) RA3-FA3
Unclassified
Relevance to DTRA’s mission/Goals of Research
• Understanding radiation induced failures of SoCs and
emerging devices helps predict their behavior and
survivability in the event of nuclear explosions.
• Long range detection/tracking of radioactive contamination
• Adsorption of radionuclides
• Methods to properly assess risk and damage on an
accelerated scale are needed by DoD in order to ensure
survivability.
3
Unclassified4
IPP Collaborations
Unclassified
Research Goals
• Advance basic science of materials interactions with ionizing
radiation and integrate crosscutting modeling/simulation related to
Survival and Response
• IPP-Systems on a chip, ultrawide bandgap semiconductors,
long-range detection of nuclear contamination, development of
radionuclide noble gas adsorbent material.
• BPP-Remote sensing of nuclear materials for isotopic science and
nuclear forensics/lightweight shielding by metallic nanostructured
materials)
5
Unclassified
How to make this a success
• What do we need to succeed?
• Understand the problem
• Assemble an interdisciplinary team-mix of wise heads, energetic
younger faculty and student pipeline
• Involve our national/defense labs
• Collaborate, collaborate, collaborate
• Keep DTRA informed and listen to their advice
6
Unclassified
Increasing Importance of Power Electronics
7
Unclassified
One example-new semiconductors used in
power switching
• Power electronics the interface between electrical source and load.
• Source and load can differ in frequency/amplitude/number of phases, and
voltages and currents can be converted from one form to another.
• egs. laptop charger 110 V ac to 19 V dc
• solar inverter convert 48 V dc to 220 V ac
• EV drive using 200 V dc battery to drive 650 V ac motor
• three-phase motor driver in hybrid vehicles, electric rail, ships
• order of magnitude improvement in power density enabled by WBG
semiconductors compared to Si (VB~Eg3.7)
Ref. Kizilyalli et al. IEEE Trans ED 62,414 (2015)
8
Unclassified
Wide Bandgap Semiconductor Power Devices
9
Unclassified
NASA-power management/distribution systems operating at 3X higher voltage
using SiC devices, reduce power losses by > 50% cf Si, mass savings >20%.
Size, weight, and power savings achieved without optimization - Si power
device design with drop-in SiC components.
SiC Schottky diodes show catastrophic SEB and other SEE at ~40% of rated
operating voltage, unacceptable degradation of leakage at ~20% of rated
operating voltage.
SEE caused by terrestrial cosmic radiation (neutrons) identified by industry as
limiting factor for use of SiC electronics in aircraft. The lunar neutron and
proton environment is expected to contribute to a hazardous radiation
environment.
10
Relevance to DTRA’s mission of countering
and defeating WMD and threat networks
Unclassified
Large bandgaps of UWBG materials and high fields
fundamentally alter mechanisms of dielectric breakdown
11
-Avalanche breakdown (impact ionization) limiting factor for power
devices.
- In conventional semiconductors, occurs when field-accelerated
electrons ionize atoms, promoting electrons to conduction band
- Can be assisted by defect states that lower barrier, and electron
tunneling. Impact ionization when KE gain by electrons from field is >
relaxation by scattering (von Hippel criteria).
-Unclear avalanche breakdown occurs in UWBG materials. Ionization
coefficient of e lower than h, bandgaps are larger than electron affinity
and conduction band
-Differences in electronic structure could lead to other dielectric
breakdown processes, bond breakage and material degradation.
Hypothesize in UWBG materials, new breakdown processes arise.
-need to understand these to understand breakdown in UWBG
semiconductors under biased total ionizing dose or single event upset.
Unclassified
Time Dependence of Ga2O3 Rectifier Degradation
12
Time series TEM BF images of defect evolution prior to
the failure at 4.8V: (a) 0s, (b) 5s, (c) 10s, (d) 14s, (e)
15.5s, and (f) HAADF STEM image after failure.Cross sectional images and SAED patterns from
rectifier before and after bias stressing at 4.8 V
to induce degradation.
Unclassified
3.1 and 3.2 SOC Survivability and Testing
• Survivability Radiation Testing and Modeling of
Complex Systems on a Chip (SOC) Wirthlin,
Goeders (BYU), Haque (PSU), J. & D. Black
(SNL)
Develop predictive model of SoC failure
susceptibility.
Conduct fault injection on multiple SoC devices
to understand failure mechanisms, refine
accuracy of models and radiation testing.
Perform radiation testing (neutron and heavy
ion) on select SoCs with custom-designed test
harnesses for real-time analysis of radiation
experiments.
Identify SoC failure mechanisms, model how
SoC properties impact failure rates, refine model
using fault injection and radiation test data
• Survivability & Response: From SOC to Single
Transistor Haque, Wolfe (PSU), Black (SNL),
Wirthlin, Goeders (BYU)
• Heuristic detection of Most Vulnerable Region
Quick, reliable SOC survivability and response testing
• Fundamental damage physics : In-operando study on
single device (um to nm) directly observe
degradation mechanisms as function of radiation/
operation.
• Limits of dose equivalence testing studied with
neutron vs. ion/proton irradiation
• atomic scale modeling and simulation validated by
experiments and guide development of survivability
test cell.
13
Unclassified
3.5. Long-range Radioactive Contamination Detection and
Tracking using Intense Laser Filaments
• Jovanovic (UM), Hartig (UF), Hammig (UM)
• fundamental physics of versatile technology to enable rapid mapping and
compositional analysis of plumes and debris to detect hazardous releases of radioactive
materials over long distances.
• addresses DoD need to prevent contamination of personnel and equipment in case of
nuclear accident/armed conflict.
• Not based on detecting a product or effects of radioactive decay
• optimize laser filament propagation over long-distance using wavefront control-
promising preliminary results; extend to uranium.
• guiding optical beams for remote detection of contamination, to enable collection of more
intense optical signal.
• measure detection limit of U and UO-containing plume produced by laser ablation.
14
Unclassified
3.7 Designed Selectivity in Zeolites for Radionuclide Gas
Adsorption and Detection
• Hartig and Nino (UF), Nenoff (SNL)
• combined computational/ experimental to identify fundamental mechanisms governing
preferential adsorption of radionuclide noble gases on zeolites and Ag-chabazite
• Employ high-throughput data mining framework MPInterfaces, which extends open-
source tools, enables rapid creation of surface structures and DFT calculations of
adsorption energies
• identify a set of zeolite structures from the database and tune their adsorption strength
and selectivity.
• investigate the adsorption of atoms and molecules in zeolite cages for a range of cations
and for different zeolite cage structures. The adsorbing species will include Ar, Kr, and
Xe and competing atoms atmospheric molecules
15
Unclassified
3.7 Designed Selectivity in Zeolites for Radionuclide Gas
Adsorption and Detection
This research builds on a productive collaborative foundation, which will be expanded
to include new students and collaborators.
formal and informal collaborators
students≥1 future
students
Connections within the IIRM URA:
• Cross-cutting modeling (CCRI) initiative
• Student sandbox
16
Unclassified17
Experimental Databases DFT
Calculations
Computational Predictions
Materials Synthesis
Experimental
Testing and
Validation
Goal: Understand mechanisms controlling adsorption of
radionuclides in zeolites, determine design rules to maximize
adsorption specificity, and experimentally validate by
synthesizing optimized zeolites, measure adsorption properties.
Computationally; Density functional theory to calculate
adsorption energies for radionuclide noble gases (Xe, Kr, and
Ar) in zeolites with different chemistry (cation) and
morphologies.
Machine learning to identify parameters controlling adsorption
Continuum modeling of the adsorption isotherms to predict
specificity and loading characteristics.
Structural design to determine radionuclide noble gas
adsorption specificity, loading, retention, and reaction kinetics.
IPP Research/Project Description
Unclassified18
Adsorption and desorption isotherms: Collected using an
accelerated surface area and porosimetry chemisorption
apparatus with controlled atmosphere and mixture of gases.
Testing:
• At the UFTR, 41Ar is exhausted and monitored through
the reactor stack.
• Zeolites will be placed in the stack and use the UFTR
radiological measurement equipment to determine the
collection and adsorption efficiencies of the materials.
Zeolite synthesis: To inform initial simulations and validate
the theoretical design rules, synthesize current state of art
Ag-chabazite by ion exchange in the presence of AgNO3 in
solution followed by filtration and drying.
• Characterization XRD, SEM,TEM,IR, Raman, NMR
0.0 0.2 0.4 0.6 0.8 1.00
5
10
15
20
Am
ou
nt
of
ga
s a
ds
orb
ed
, m
mo
l/g
P/P0
O2@77K
N2@77K
Representative gas adsorption curves
for advanced zeolites with enhanced
O2/N2 selectivity ( Nenoff, SNL)
IPP Research/Project Description
Unclassified
Potential Impact:
Improved SOC, power electronics survivability-use full potential of these technologies
Rapid mapping/compositional analysis of plumes and debris collected on surfaces to
detect hazardous releases of radioactive materials over long distances.
Improved adsorption of radionuclides in zeolites, by design
Current and future potential impacts and contributions to other RAs and FAs
-strong linkage to Sandia, NRL
-collaboration with modeling, optimization of materials, devices/integration- CCRI, RA1,
RA2, FA1-4
24 graduate students, undergrads, or post-docs supported-ECE, Nuclear Eng., Mech
Eng, Materials Sci., Chem Eng
19
Potential Impact
Unclassified
Risks/Transitions/Additional Collaboration
• Potential risks for completing research in next 18 months
-Nothing obvious, virus situation deteriorates to extended lockdown
• Potential transitions of research results to applied research
- Design of improved Ga2O3 rectifiers (able to determine most
important outcome of crystal growth and device structure)
- Improved fault analysis libraries for SOC
- On –board laser detection/tracking systems
• Potential for collaborations within the larger scientific
community
- MURIs on Ga2O3 (AFOSR- UCSB, Cornell, OSU/BYU)20
Unclassified
spare-Energy Consumption US 2019
21
Unclassified
Spare-US Electricity Flow 2018, quadrillion BTU
22
Unclassified
RA3/FA1: Survivability & Response
Name: Aman Haque; Fan Ren & Stephen Pearton (UF); Mike
Wirthlin & Jeffrey Goeders (BYU)
Date: August 27, 2020
Penn State University
23
Unclassified24
RA3: FA1 Overview
3.3 Electrical and Structural Characterization of Radiation
Damage in Ultra-Wide Bandgap Semiconductors.
Pearton, Ren and Hartig (UF), Haque (PSU), Khachatrian (NRL)
3.1 Survivability Radiation Testing and Modeling of Complex
Systems on a Chip (SOC).
Wirthlin & Goeders (BYU), Haque (PSU), Black (SNL)
3.2 Survivability & Response: From SOC to
Single Transistor.
Haque, Wolfe (PSU), Black, (SNL), Wirthlin &
Goeders (BYU)
Unclassified25
Division of Roles
Most Vulnerable Region (MVR) Detection
Ultra-wide Bandgap Semiconductors
Unclassified26
IIRM Relevance:
Understanding radiation induced failures of modern SoCs helps
predict SoC behavior and survivability in the event of nuclear
explosions.
Research Goals/objectives:
• Identify SoC failure mechanisms
• Model how SoC properties impact failure rates and severities
• Refine model using fault injection and radiation test data
3.1 Radiation Testing and Modeling of SOCs
Unclassified
Modern SoC are highly complex devices with many interacting components.
Internal memory and register states are susceptible to radiation upsets
Major Research Questions:
a) How do modern SoCs behave when irradiated?
b) What are the different failure modes and mechanisms?
c) How do the various SoC components contribute to system failures?
d) How can radiation and fault injection testing results be used to best predict the
behavior of other, non-tested SoCs?
3.1 Objectives
27
Unclassified
• Single-Event Effect Radiation testing of Complex Devices
• Field Programmable Gate Arrays (FPGA)
• System-on-Chip Devices (SOC)
• FPGA SEE Mitigation Techniques
• Automated Triple Modular Redundancy (TMR) Tools
• Configuration Scrubbing
• High-Performance Reliable Computing
• High-Data Rate Sensor Processing
• Machine Learning
Prior Works
D Q D Qlogic
D Q D Qlogic
voter
voter
voterD Q D Qlogic
voter
voter
voter
28
Unclassified
• Automated DWC/TMR software protection
• Works with ARM, ARM64, RISC-V, MSP30
• Tested in neutron beam at LANSCE
• High degree of user control:
• Choose which functions or which variables to protect
• QEMU-based fault injection framework
C Program
(or other language)
COASTCompiler w/
Protection Pass
Executable
DWC/TMR
Protection
Prior Works
https://github.com/byuccl/coast
COAST (COmpiler-Assisted Software fault Tolerance)
29
Unclassified
• Modern SoCs contain complex CPUs
• Multiple processor cores
• Multi-level caches and embedded memory
• MMUs, debug units, vector instructions and more
• Large amount of dedicated I/O interfaces
• Network controllers
• Encryption engines
• DMA
• Power and security management
• Multiple I/O and memory controllers
- How susceptible are components to ionizing radiation?
- How do failed components impact overall system?
Challenge: Complex System on Chip (SoC)
Example SOC System: Xilinx MPSOC
30
Unclassified
Models:
• Predict behavior and error rates using SoC properties
• Pros: Fast, can be applied to untested parts
• Cons: Limited accuracy, components in one SoC may not exhibit
same failures in other systems.
Fault Injection:
• Modify registers or SoC internals using custom software, simulation
frameworks, or debug interfaces
• Pro: Can target specific components for test
• Cons: Not all internal state may be accessible
Radiation Testing:
• Pro: Most accurate
• Cons: Time consuming, expensive, and difficult to fully evaluate
complex device
Approach
Models
Probing, Fault Injection
Radiation and Laser Testing
AccuracyEase of Use
31
Unclassified
Subtask Goal: Develop predictive model of SoC failure susceptibility
based on SoC properties and components.
Year 1-2:
• Develop database of common SoCs and their characteristics
• Incorporate existing radiation test data into SoC database
• Assemble high-quality benchmark suite that represents a range of realistic SoC
workloads.
Cross-Cutting Plans:
• Incorporate results from Ion-Beam tests in RA3-FA2
• Use results from Most Vulnerable Region (MVR) studies from RA3-FA1, 3.2
Subtask #1: SoC Model and Test Tools
Device Models
32
Unclassified
Subtask Goal: Conduct fault injection on multiple SoC
devices to understand failure mechanisms, refine accuracy
of models, and to help target and refine radiation testing
experiments.
Year 1-2:
• Testing Xilinx MPSoC chip (ARM A53 architecture)
• Building a software suite that exercises various SoC
components
• Leverage ARM debugging interfaces to build a framework
that can inject errors, and capture execution traces
Cross-Cutting Plans:
• Calibrate FI results and Models against in-situ ion beam tests
from RA3-FA1, 3.2
Subtask #2: Fault Injection
33
Unclassified
Subtask Goal: Perform radiation testing (neutron and heavy ion)
on select SoCs with a custom-designed test harnesses to perform
real-time analysis of radiation experiments.
• Results compared against fault injection and model, and iterative
improvements made where possible
Year 2:
• Design and development of initial testing framework and tools, for use
in radiation testing in later years.
• Extract experimental data from ongoing radiation testing in other funded
research projects. This will serve as a guiding baseline for later year
experimental testing.
Cross-Cutting Plans:
• Support for in-reactor studies completed as part of RA3-FA1, 3.2
Subtask #3: Radiation Testing
34
Unclassified
The different approaches will have a synergistic relationship:
• Models (from our work and from cross-cutting testing), will
guide and predict fault injection and radiation testing
• Fault injection results will help guide radiation testing, ensuring
the limited radiation test time will be spent testing the
• Radiation testing results will be analyzed to determine accuracy
of predictive models and fault injection
• Unexpected behaviors will be analyzed to derive new models and
fault injection approaches
Synergy between Sub-Tasks
Predict emerging SoC behavior
Validate fault injection results
Improve model using experimental data
35
Unclassified
Potential Collaboration: Los Alamos
Neutron Science Center (LANSCE)
• Irradiation of Chips Electronics (ICE House)
• High flux neutron facility for electronics testing
• Energy spectrum close to terrestrial neutron spectrum
• Potential Collaboration
• Investigate neutron testing approaches for SoC devices
• Perform neutron radiation SEE testing on SoC devices
• Contact: Dr. Stephen Wender, Ph.D.
36
Unclassified
3.1 Roadmap
3737
TASK Y1 Y2 Y3 Y4 Y5
I. SoC Modeling and Tools 1st
half
2nd
half
1st
half
2nd
half
1st
half
2nd
half
1st
half
2nd
half
1st
half
2nd
halfSuccess Metrics
• Build database of common SoCs and properties
• Benchmark suite of realistic SoC workloads
• Build and iteratively improve SoC fault model
• Incorporate injection/radiation results into model
• How well model predicts
results obtained from fault
injection and radiation
testing.
II. Fault Injection 1st
half
2nd
half
1st
half
2nd
half
1st
half
2nd
half
1st
half
2nd
half
1st
half
2nd
halfSuccess Metrics
• Develop probing tools using ARM debugging interface
• Develop robust fault injection programs
• Advanced fault injection into wide range of modules
• Guided fault injection using enhanced models
• Error profile of SoC
components
• Speed and coverage of fault
injection
III. Radiation Testing 1st
half
2nd
half
1st
half
2nd
half
1st
half
2nd
half
1st
half
2nd
half
1st
half
2nd
halfSuccess Metrics
• Develop initial testing frameworks and tools
• Neutron radiation testing of common SoCs
• Improve radiation test tool to allow high-speed analysis
• Expand testing to high-flux environments
• Monitoring of diverse peripherals
• Coverage of components
that can be monitored
• Identification of most
vulnerable components
Unclassified38
Personnel
PI Mike Wirthlin, Ph.D. Dr. Wirthlin will lead the SOC fault injection and SOC
radiation testing effort.
Co-PI Jeff Goeders, Ph.D. Dr. Goeders will lead the SOC failure modeling effort and
SOC processor testing integration.
Grad
Students
T.B.D. Two graduate students (not yet identified) will be hired to
support each faculty member.
Undergrad
Students
T.B.D. Two Undergraduate students will be involved to support the
graduate students and support workforce development.
Unclassified
Potential Impact
• Identify more efficient methods to assess risk and damage of SoCs
• Understand the fundamental interaction and degradation
mechanisms of COTS devices
• Provide tools and novel approaches for facilitating analysis of failure
mechanisms complex SoC systems
39
Unclassified40
Research Goals/objectives:
DOI: 10.1109/VLSI-SoC.2016.7753568
• 3.2A: Quick and reliable SOC survivability and response testing.
• 3.2B: Fundamental damage physics (Experiment Modeling).
• 3.2C: Dose equivalence testing.
3.2 SOC Survivability & Response
Unclassified41
Current Art: Extreme integration of hardware, software and irradiation units.
Challenges: Complexity multiplied by all possible device, test, irradiation configurations
10.1109/TNS.2014.2342872
3.2A SOC Survivability & Response
Unclassified42
Research Goals/objectives:
Fast, device independent MVR detection
Heuristic detection of the Most Vulnerable Region (MVR) of a SOC
3.2A SOC Survivability & Response
Black-box Philosophy: Pre-existing
defect/damage density as a predictor
and temperature as a sensor.
Device design; material; fabrication
processing; scaling govern
vulnerability to damage accumulation
Analogy to mechanical fatigueOnset of permanent damage as a
predictor for vulnerable regions
Unclassified43
Technical Approach:
3.2A SOC Survivability & Response
Breitenstein & Sturm, QIRT 2018
Sensing shorts between power or signal lines, oxide or junction
breakdowns, high resistive plugs, latch-ups with thermal signature
Unclassified44
Technical Approach: Lock in thermography
3.2A SOC Survivability & Response
Periodic input energy wave penetrates
surface to be absorbed and phase
shifted.
Defects/interface/inhomogeneity
partially reflects the input to develop a
phase shift.
Acquire signals at a specific frequency
and eliminate other
Unclassified45
Technical Approach:
(A)Map pre-existing defect density
Hypothesis: Cross-section vs LET profile
is uniquely related to characteristic
operating temperature vs LET profile.
3.2A SOC Survivability & Response
Radiation specific isolation of MVR
Outcome:
Preliminary isolation of MVR w/o any
device specific information
Device specific defect density vs
operating temperature relationship
(B) Map post-radiation defect density
(D) Cross-validate with conventional methodology {ripe for machine learning}
(C) Power up DUT with intentional ‘errors’
for lock-in thermography
Unclassified46
Technical Approach: In-situ high resolution microscopy (TEM/Kelvin Probe/THz)
In-operando study on single device (um to nm)
directly observe the degradation mechanisms as
function of radiation and operation.
3.2B Fundamental Damage Physics
Unclassified47
In-operando TEM to directly observe the degradation
mechanisms as function of radiation and operation.
Ion-beam and laser equipped TEM @ Sandia;
In-situ transistor operation in TEM @ Penn State
3.2B Fundamental Damage Physics
Unclassified48
3.2B Fundamental Damage Physics
Ga2O3 diode:
In-operando failure
mechanisms observation
Unclassified49
3.2C: Dose Equivalence Testing
Limits of equivalence will be studied by studying with
neutron vs. ion/proton irradiation.
PSU Breazeale
nuclear reactor core
Radiation test set up in
beam lab
In-situ radiation detection can measure current
generation from gamma ray radiation
Unclassified50
Limitations & Risks
Cross-section vs LET profile not uniquely related to characteristic operating
temperature vs LET profile.
In-operando TEM boundary conditions not scalable to actual device
Unclassified51
Benefits of Proposed Approach
Quickest possible MVR detection
Direct visualization of damage physics
Reliable dose equivalence testing
Impact beyond RA3/FA1
Ionizing effects in novel materials/sensors (RA1)
Damage mechanics & reliability of devices (RA2)
Cross-cutting modeling & simulation
Unclassified52
Performance Targets
TASK Y1 Y2 Y3 Y4 Y5
I. SOC Survivability & Dose Equivalence 1st
half
2nd
half
1st
half
2nd
half
1st
half
2nd
half
1st
half
2nd
half
1st
half
2nd
halfSuccess Metrics
• Lock-in thermography development on SOC
• Feasibility of MVR detection (ion irradiation)
• Cross-validation protocol with BYU collaborators
• Defect mapping (25 um)
• Ion irradiation
demonstration
• Neutron based MVR protocol development
• Pulsed laser based MVR validation (BYU)
• Setup for neutron beam dose equivalence (D. Wolfe)
• Neutron based MVR demo
• Ion/Neutron equivalence
• Cross-SOC/Cross energy MVR protocol validation
• Ion/ laser/Neutron dose equivalence (BYU/Wolfe)
• Multi-SoC MVR
• Multi-source MVR
II. Fundamentals of Damage Physics 1st
half
2nd
half
1st
half
2nd
half
1st
half
2nd
half
1st
half
2nd
half
1st
half
2nd
halfSuccess Metrics
• Radiation effects on UWBG films (UF)
• Single UWBG device lock in thermography
• Radiation hardness
characterization
• Thermal NDT on UWBG
• Pulsed laser SEE post-mortem (UF, NRL)
• Single UWBG in-operando TEM or THz (UF, NRL)
• Diode SEE mechanism
• Real time damage
visualization
Unclassified53
Personnel
PI Aman Haque, PhD Dr. Haque will lead the efforts (a) Most vulnerable region
detection in SOC (b) Fundamental Damage physics and ©
Dose equivalence.
Co-PI Doug Wolfe, PhD
PostDoc TBD A post doc will appointed for the research at PSU
Grad
Students
John Sherbondy (1st year PhD
student)
All students will be exposed electronics performance and
reliability studies, radiation effects and in-operando
microscopy to train the future generation DTRA skillsets
Unclassified
• Current and future impacts to other RAs and FAs
Proposed in-operando microscopy (lock-in thermography/TEM/THz) can be
readily applied to RA 1 (new materials characterization), RA 2 (device
performance and damage physics). Direct visualization of these processes can
help quick and accurate modeling and simulation.
54
Impacts
• Within the larger scientific community
NDT thermography on rechargeable battery performance and health
monitoring; Biomedical imaging of tissue irradiation
Unclassified
RA3 – FA2 & 3
Collection and Analysis
RN Contamination
Igor Jovanovic
University of Michigan
Co-PI Kyle C. Hartig
University of Florida
August 27, 2020
55
Unclassified56
Overview
This research builds on an existing and productive collaborative foundation, which is
being expanded to include new students and collaborators.
Synergy with NNSA MTV
consortium and other
existing awards (e.g.
DTRA YIA, NSF, ONR) formal and informal collaborators
students
DOD SMART
fellows
studentsNSF GRFP fellows
Unclassified57
Relevance to DTRA warfighter
• Large-scale radiological releases
pose an obstacle to DoD operations
on land, in air, and at sea
• Determine the location, extent, and
movement of treat → safer
maneuvering and protect personnel
and equipment
• Understanding the nature of threat
(element, isotope, compound) →
prediction of transport and decay
This is difficult to do by remotely detecting ionizing radiation:
its propagation range in air and number of quanta are limited!
Unclassified58
Technical approach: detect contamination by
delivering excitation through laser filaments
Time-resolved spectroscopic signature
Optical photons can propagate over kilometers
with little attenuation in atmosphere and can be
effectively collimated.
Unclassified59
Femtosecond filaments can be used to remotely
detect uranium, its compounds, and isotopes
Filament molecular isotope
shift detection in UO
K. Hartig et al., Sci. Rep. 2017
Fluorescence of UO2F2 induced
by filament conical emission
P. Skrodzki et al., Sci. Rep. 2017
Single-shot, multi-signature
uranium detection
L. Finney et al., Opt. Lett. 2019
Unclassified60
Research objective: understand the relevant
physics that governs this method of detection
Which properties
of optical beams
affect the
propagation
range?
How is the characteristic optical signal generated in
filament-matter interactions in solids, gases, and aerosols ?
What is the optimal
method to detect optical
signal and distinguish it
from background?
Can the the 1/r2
limitation be
overcome in signal
collection?
Unclassified61
Synopsis of R&D in RA3 - FA 2&3
Beam propagation Signal generation Signal collection
no phase plate with phase plate
M. Burger et al., CLEO 2020 P. Skrodzki et al., Sci. Rep. 2018 P. Skrodzki et al., Sci. Rep. 2017
Unclassified62
IPP Performance targets and metrics
• Implementation of effective shadowgraphic and species emission imaging with
nanosecond resolution
• Time-resolved measurement of plasma composition, temperature, and density near
target relevant to spectroscopic signal generation
• ≥2 journal papers and ≥2 conference presentations
• On-boarding of 5 new graduate students (3 @ UM and 2 @ UF)
• ≥4 student and/or faculty internships/research fellowships at national or DoD labs
• ≥1 joint experimental campaigns between UM and UF groups
Unclassified
• Could overcome certain limitations of ionizing radiation detection for contamination
detection in open areas over long standoff distances
• Does not rely on measuring secondary effects of radiation, such as ionization in air →
has the potential to provide refined understanding of the nature of threat and its evolution
• Collaboration with scientific communities: environmental sensing, atomic, molecular,
and plasma physics
• Potential transition to applied research: portable devices that could monitor pollution and
nuclear proliferation signatures
• Risks: restricted experimental facility access and new student training, supply delays, and
restricted travel
63
Potential Impact
Unclassified64
Questions