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