Detector Electronics and Signal Processing,
Non-traditional Computational Devices)
Enhance detectability and survivability for the warfighter by:
• Understanding how new materials and electronic
components (scintillator/photosensor) interact with each
other, and with ionizing radiation.
• Improving the speed, size, and radiation sensitivity of the
data acquisition circuitry
determine which radiation effects are dominant and what
impact they have on mission performance (Conventional
Nuclear Integration).
features that can be applied to improve the performance
and the integration of the non-detection components of a
detector system 2
(3) data acquisition and processing
(4) accurate extraction of detection and imaging features.
=> Aided by collaboration between personnel with orthogonal specialties (e.g. expert in
machine learning coordinating with experts in radiation detection).
• IPP-Inorganic-organic photosensors, Radiation-effects on negative capacitance
materials and FeFET Non-volatile Memories and architectures, Data-acquisition and
weakly-supervised learning algorithms
computing architectures, more sophisticated algorithmic techniques including those based on
multiple sensors and interactive sensing strategies. 3
Unclassified
- We don’t have a low-cost, low-voltage, high quantum
efficiency, large-area scintillator readout available
=> photovoltaic community designs specifically for
these parameters.
rate), limited areal extendability, and high cost.
- Challenge: High capacitance associated with thin-film
devices can attenuate charge pulse. (use capacitance-
insensitive readout). Demonstrate novel device physics
via metrics that are informative to pulse-mode radiation
detection.
4
one can remove inadequacies of photosensor (small-area
deployment, poor quantum efficiency, timing, noise,
high-dose sensitivity) from limiting the performance of
the overall detection system.
5
- UM will test the photosensors. UNC and UM will collaborate on device design. Both groups will collaborate
with RA1 on the perovskite work and the automated material search (with MIT, Northwestern).
• 4.2.1a Zero bias, High Efficiency, Angle-Insensitive Optical Photon Detection from
Ultrathin Fabry-Perot Cavity Sensors (PI: Hammig, UM)
• 4.2.1b High gain, low-noise perovskite photodetectors (PI: Huang, UNC)
- Radiation effects and hardness evaluated beyond IPP. (expected for wider band-gap, thin-films, diffusion).
- IIRM-Impacts: RA1: FA1 (Advanced Semiconductors), FA2 (Advanced Scintillators), Automated Synthesis,
RA2: FA2/FA4 (Radiation Effects, Device and Modeling), Cross-cutting (Device Simulation, Radiation Effects)
- Broader Impacts: Optical sensors for low-light detection (e.g. fingerprint scanner), photovoltaics
UNC UM
Focus Areas 2 & 4: Radiation effects in Emerging Non-volatile Memory
and Non-traditional Compute Fabrics
2) Understand the basic mechanisms of ionization induced
charge generation, collection and transport in these
devices;
(TID), high dose rate and single event effects (SEE)
through a combination of materials composition
engineering, device design enhancement; and array
level redundancy and sensing improvement, and
4) Establish a multiscale materials, devices, and circuits
simulation platform that will be used to mitigate the
impact of radiation exposure using novel electrical
masking techniques to improve their resilience to
adversarial attacks. 6
Datta, Notre Dame, Juan
- IIRM-Impacts: RA1: FA1 (Advanced Semiconductors), RA3: FA1 (Survivability Radiation Testing), Cross-
cutting (Device Simulation, Radiation Effects)
- Broader Impacts: Continued computational performance scaling impacts semiconductor industry. DoD
compatible components supported by industrial base.
Unclassified
Detector Electronics and Integration
adaptive, intelligent, and learning ADCs:
1) High-performance flexible ADCs will deliver efficient digitization and
flexible performance supporting a wide range of radiation detectors and
also enabling per-pixel digitization
2) Intelligent, aware ADCs will directly extract information and make decisions.
3) Event-driven digitization will greatly reduce the amount of data generated.
Algorithms: Develop general data handling and classification techniques that can
be broadly applied across radiation detection and imaging problems.
- Beyond “what’s in the box” searches, develop ML detection and imaging
algorithms for variable, potentially unknown high-background environments
(e.g. post-detonation environments).
- By end of research, have optimized algorithmic approached (versions of WSL,
LCA) that can be coupled to enhanced materials (RA1) and rad-hard readout
technologies (RA2) so that radiation mapping can be realized in portable,
affordable instruments designed to accomplish DoD mission sets. 8
Unclassified
Area 3, Detector Electronics and Integration
9
• 4.2.3. Intelligent, Adaptive, Learning ADCs for Radiation Digitization and Detection (PI: Flynn, UM)
• 4.2.5a Weakly Supervised Learning for Detection and Mapping in the Presence of Uncertain Environments (PI, Scott, UM)
• 4.2.5b Development of Machine Learning Algorithms for Radiation Imaging using Single Detector, Portable (PI: Bevins, AFIT)
• 4.2.5c. Advanced Analysis Techniques for Gamma Ray Spectra (PI: Lintereur, PSU)
-IIRM-impacts: Algorithmic work and ADC innovations are intended to be detector agnostic (RA1, RA2: FA1),
Cross-cutting (Device Simulation, Radiation Effects)
-Broader impacts: Any highly variable background environment (e.g. Active interrogation, Army (IED), DHS),
Efficient and Intelligent Digitization
RA1, FA2 (Scint)
• High potential to transition research results to applied research
• Training the next generation workforce: 22 graduate students, undergrads, or post-docs supported-
Electrical Eng, Nuclear Eng, Mech Eng, Materials Sci, Chem Eng, Appl Phys.
RA1 (New
Jinsong Huang2, Mark Hammig1, Jay Guo1
8/27/2020 1 University of Michigan 2 University of North Carolina at Chapel Hill
Unclassified
Overview
12
• FA 1 (Photosensors)
Fabrication, Radiation Testing)
Engineering)
2 undergraduate students
design)
1 graduate student,
1 undergraduate student
for Radiation Sensing
• Radiation detection technologies to support the interdiction of nuclear and
radiological materials are critical to combating nuclear and radiological
terrorism.
• Photon conversion: scintillators + photodectors
Pros: low noise (cooled), high gain
Cons: large volume, fragile, high cost, strict voltage & power requirement, susceptible
to magnetic field
• Light weight, robust, solid state, weak light sensors are needed
13 PMT
• Silicon PMs are array silicon micro-size
photodetectors
Geiger mode
14
Limitations:
• Price per cm2 is one order of magnitude higher than
PMT;
• Noise (small Eg) scale up with the area of the detector
• Large gain drift
• QE limited at near UV.
Unclassified
15
hν: Energy of incident photons (eV)
This equation applies only if the total noise of a device is dominated by dark-current
determined shot noise.
Specific Detectivity (D*)
High gain type
High gain, low
current) coupled to
have scintillator
photon production
limit counting
statistical noise
Objective #1: Extremely low noise photodetectors with zero bias.
Objective #2: Describe the photon-interaction physics such that device
structures with high EQE and angular insensitivity can be modeled for
arbitrary scintillator emission spectra.
Objective #3: Validate the modeling by reading out various scintillators with
an a-Si cavity sensor such that the resulting detector has comparable or better
resolution to a scintillator coupled to a PMT or an SiPM.
Technical Objectives
Fabry–Perot (F–P) resonator to produce the
desired reflected colors, which are determined
by the thickness of the semiconductor. The
two metal layers also simultaneously function
as electrodes.
19
photoactive layer ranges from 10 nm to
27 nm (an order of magnitude smaller
than that of the typical p–i–n a-Si).
- The reflected colors are neither
sensitive to the angle of incidence for
angles of up to 600 nor to the
polarization state of the incident light.
Unclassified
20
- The thicknesses of the a-Si layer for the CMY colors are 27, 18 and 10
nm, respectively
- Match in measured EQE and simulation absorption indicates that most
of the absorbed photons are harvested and contribute to the
photocurrent with negligible electron–hole recombination (due to thin
active layer relative to typical charge-diffusion length in a-Si.
Unclassified
21
- NaI(Tl) emission peak at 415 nm.
- Reflection from the stack is only 5.3 % at 415 nm with a 6.2
nm a-Si absorption layer.
Unclassified
- SrI2 (Eu) emission peak at 440 nm.
- Simulated variation in the 137Cs spectrum derived from a SrI2(Eu)
scintillator and optical-cavity photodiode with either 60 % peak EQE or 90
% EQE, improving the energy resolution from 3.7 % to 2.8 % or 2.3 %,
respectively.
22
Unclassified
Photodetectors
High Signal
Large Linear
Dynamic Range
Fast Response
Intrinsic semiconductor
Fast and Low cost fabrication
Stability is much less an issue after years development for solar cells.
24
Unclassified
ITO
PTAA
Perovskites
PCBM
Cu
• Low noises enables observing weak light
1 2 3 4 5 6 7
10 -14
10 -13
10 -12
10 -11
10 -10
10 -9
10 -8
S ig
26,000
2,700
290
37
3.8
1.5
0.64
Dark
Decreasing
400 500 600 700 800 0
20
40
60
80
100
Unclassified Shen, Huang et al., Adv. Mater. 28, 10794, 2016
0.0 0.5 1.0
Operate at zero bias
• Device response speed is limited by the RC constant; • Reducing device area (c) increased the device respond speed to GHz, limited by laser
pulse width.
response spectrum, reduced noise and better stability;
Objective #2: Large device area, and further reduced the
photodetector noise using doping controlling and improved grain
size and crystallinity;
such that the resulting detector has comparable or better resolution
to a scintillator coupled to a PMT or an SiPM.
28
Unclassified
Large Jd No gainCons
Small Jd Big R Can we combine the merits of two
types of photodetectors?
Types of Solid-State Photodetectors
In dark Under illumination
300 350 400 450 500 550 600 650 700 0
20
40
60
80
100
P3HT:ZnO
PVK:ZnO
F. Guo, et al , J. Huang*, Nature Nanotechnology, 7,798–802 (2012) 31
Unclassified
• Still reserve the low dark current of a photodiode (Schottky Junction)
Device Performance- Dark Current and
Photocurrent
PhotoconductorsPhotodiodes
-10 -8 -6 -4 -2 0 2 4 6 8 10
10 -8
10 -7
10 -6
10 -5
10 -4
10 -3
10 -2
10 -1
10 0
Dark current
J (A
/c m
Unclassified
Using Perovskites
Chen, Huang* et al, Nature Communications, 8, 1890 (2017) Ni, Huang* et al., Science 367, 1352 (2020)
MAPbI3 MAPbBr3
- Charge sharing induces SNR
detectors (2 mm to 1 cm diameter
silicon devices impinged by alpha
particles)
Validation of Charge-Sensitive Amplifier Configuration that
Compensates for Detector Capacitance”, IEEE TNS 63 (April
2016).
Vout
• Stable performance even with higher detector capacitance, enables large-area semiconductor detectors:
- pixellated imagers (with cathode and anode grid)
- structured conversion-type silicon-based neutron detectors
- nanocrystalline colloidal solids
• Fast rise time preserved.
• Applicable to any other applications such as biomedical readout circuit, large pixel CCD camera, optical
communication system, etc.
Capacitance presented to Charge-Sensitive
Vout
- Amplitude is preserved.
- Innovation is critical for large area devices (> 1 dm2) or thin-film
sensors, or materials comprised of high permittivity materials.
2 mm
10 mm
a particle
• Hammig group will collaborate with Guo’s group for Angle-
Insensitive Optical Photon Detection ;
• Huang group will fabricate high sensitivity high gain or low noise
perovskites photodetectors;
compare them with other types of photodetectors
37
Unclassified
Other Research Area Impact:
• The development of high quality perovskite single crystals with large mobility, low
noise, and high stability will also facilitate the development of perovskite radiation
detectors; Circuitry work will enable perovskite and nanosemiconductor detectors
(RA1, FA1)
Transitions:
- The understanding of defect physics and chemistry in perovskites will accelerate the
development of other perovskites based technologies, particularly medical imaging,
optical imaging, and photovoltaic technology for clean, renewable energy;
- The fundamental understand of material chemistry will enable future material growth
scaling up in industry.
volatile Memory and Non-traditional
Compute Fabric (FA2, FA4)
8/19/2020
Smartphones (CMOS scaling, RF
42
Exponential growth in ML model sizes are drivers of hardware growth
AlexNet
NIN
DNNs in academia without optimization
DNNs in academia with optimization
Xiaowei Xu, et al., “Scaling for edge inference of deep neural networks,” Nature Electronics 2018
Unclassified
Utilization and throughput limited by data movement
[1] N. P. Jouppi et al., “In-datacenter performance analysis of a tensor processing unit,” ISCA 2017
[2] Y. H. Chen et al., “Eyeriss: an energy-efficient reconfigurable accelerator for deep convolutional neural networks,” JSSC 2017
Unclassified
Intel Haswell
44
Unclassified
DRAM and SRAM to accelerate in-memory compute
Unclassified
Unclassified
HZO (FE)
W (G)
n+ (D)
CMOS compatible highly scaled Ferroelectric FET using ALD
Hafnium Zirconium Oxide (HZO)
48
S. Cheema , S. Salahudidin et.al, Nature 2020
Unclassified
49
Pseudo cross-bar array with analog ferroelectric weight cells for online training & inference
Unclassified
50
search pattern and stored pattern
50 100 0.0
Switching matrix
Search pulses
under regular and harsh environment
Unclassified
An existence proof that endurance can be high
Unclassified
53
Unclassified
Objective #1: Through experiments and modelling at the device and array
level explore the physics of interactions between radiation and carrier
generation in FeFET (and Ferro capacitor) under varying electrical bias
conditions (Datta, Nino)
Objective #2: Engineer the ferroelectric material and the interfacial oxide layer
(thickness, dielectric constant) to harden FeFET (and Ferro capacitor) for
immunity to total ionizing dose (TID), displacement damage (DD) and single
event error (SEE) (Salahuddin)
of radiation on performance of in-memory FeFET compute fabric (cross-
point & TCAM array) (Datta, Das, Salahuddin)
Objective #4: Engineer the FeFET devices to increase the immunity of
FeFET compute fabrics to total ionizing dose (TID), displacement damage
(DD) and single event error (SEE) in harsh environment
(All)
- electrons (TD; DD)
57
o Post-irradiation damage (ionizing and non-ionizing)
o Technological modifications to enhance robustness
Single Event Effects (devices and circuits)
o Generated charge profiles
o Logic masking and temporal masking steps to enhance
robustness
Unclassified
high gate bias removes electrons through gate electrode
Holes drift in the oxide slowly (by hopping transport)
Holes can trap in deep levels close to silicon or IL
Holes can create bulk fixed charge or interface traps
Unclassified
0
1
2
3
4
5
61
-20
0
20
40
Unclassified 62
(COMSEC/OPSEC) areas via UF’s Florida Applied Research in Engineering
(FLARE).
Capacity and facilities to handle secret and ultimately special-access program and
sensitive compartmented information.
The UFTR is the only Argonaut-type reactor that is
operational and can release 41Ar at approximately 120 μCi/sec
during normal full power (100 kW) operations into the
environment through either a low- or high-velocity stack.
The Gamma Irradiator Facility (60Co - 1.17 & 1.33 MeV) for
total ion dose experiments (TID).
Unclassified
• Nino will receive FeCaps and FeFETs and perform:
• Perform TID experiments using UF’s Gamma Irradiator Facility
• FeCaps :Electrical Characterization (capacitance voltage (C-V), polarization-
voltage (P-V), retention, endurance, and impedance spectroscopy. Both pre and
post irradiation
slope, read window. Both pre and post irradiation
• Datta/Salahuddin will do TID modeling for FeCaps and FeFETs
• Das will do modeling of effect of TID on FeFET binary and analog weight cell
64
Scott (Michigan) and James Bevin (AFIT)
August 27, 2020
• Clay Scott, Michigan, Machine Learning and Training
• James, Bevins, AFIT, Imaging and machine learning
• Michael Flynn, Michigan, Mixed-signal electronics
66
Unclassified
Goals
• Faster and more robust– fewer samples
• Learn the signal
• Smarter electronics
68
Detector
• CNN, ANN, FC-NN
• Extract signal information
Existing datasets
Data simulation
o Noisy labels from contaminating sources
o Background shift between training and testing environments
o Biased simulations
• Coordinated sensors provide weak
supervision sufficient for learning
72
[Scott]
Unclassified
73
Truth
ReGeNN
Reconstructions
events (inefficient) and low
2% Noise Algorithm development is required to
handle full spectrum counts (background
variance), fast collection times (statistical
variance), and increase the robustness
ReGeNN w/
5% Noise
dependence
76
• Low power, fast features