2023/03/31 Chia-Hao Lee's PhD Defense @ UIUC Supercon 2008 Advisor: Prof. Pinshane Huang Committee: Prof. Pinshane Huang, Prof. Jian-Min Zuo, Prof. Andre Schleife, Prof. Vidya Madhavan Youtube recording: https://youtu.be/oJhY6ZOJabo Personal website: https://sites.google.com/view/chiahao-lee Research Summary: My research explores the use of advanced microscopy techniques and machine learning algorithms to understand the heterogeneities of two-dimensional (2D) materials. While 2D materials exhibit a wide range of unique properties that make them ideal candidates for various applications, including flexible electronics, energy conversion, and catalysis, their properties can vary significantly due to their heterogeneity, which arises from the presence of defects, grain boundaries, and other structural imperfections. I combined the class-averaging technique with deep learning models for defect identification to generate high signal-to-noise images of single-atom defects. These images provide the 1st direct observation of oscillating strain fields around a single atom vacancy with sub-pm precision. Additionally, I co-developed an AI-in-the-loop framework that combines a cycle generative adversarial network with automatic acquisition and image simulation. This framework generates high quality training data that greatly enhances the generalizability of machine learning applications. Furthermore, I explored the anisotropic phase transition kinetics of few-layer MoTe2, a promising phase-change material, using in situ heating, dark-field TEM, and aberration-corrected STEM. Most recently, I applied electron ptychography on 2D materials and obtained unprecedented details about their local lattice distortion and rippling with 0.4 Å resolution, greatly surpassing the conventional approaches. In summary, my research demonstrates a combination of new S/TEM techniques with machine learning, enabling atom-by-atom characterization of heterogeneities of 2D materials including phase boundaries, strain, point defects, and local rippling with high precision. Overall, these techniques pave the way for the development of reliable and efficient 2D electronics, making significant contributions to the field of nanotechnology.

1. Characterizing the Heterogeneity of 2D Materials with
Transmission Electron Microscopy and Machine Learning
Chia-Hao Lee
Advisor: Prof. Pinshane Y. Huang
University of Illinois Urbana–Champaign
2023.03.31
GAN-enhanced realistic
simulated image
1 nm
Input Output
Deep sub-angstrom resolution
imaging of 2D materials
Sub-pm precision measurement
of single-atom vacancy
5 Å
pm
10.0
0.0
-
-
-
5.0
-
-
7.5
2.5
5 Å

2. Acknowledgments
Dr. Di Luo Prof. Bryan K. Clark
Abid Khan Chuqiao Shi
Prof. Arend van der
Zande
Dr. M. Abir Hossain Prof. Pinshane Y. Huang
Main Collaborators
Yue Zhang
FA9550-17-1-0213 DE-SC0020190
Huang Group
2 / 38

3. Why 2D materials?
1. 2D confinement-induced physical properties
2. High tunability of properties via defects and strain
3. Flexible heterostructures design
In WSe2, 0.4% tensile strain can enhance the e- mobility by 84%
3 / 38
Geim, A. K. (2013), Nature
Because they’re atomically thin……
Atom-by-atom characterization
routinely achieved with
aberration corrected STEM
WSe2-2xTe2x
Annular Dark-Field STEM image
5 nm

4. STEM as an atomic-scale characterization tool
4 / 38
Electron - Sample Interactions
Z+
Annular
Detector
Low angle
scattering
Inelastic
scattering
High angle scattering,
Annular dark-field (ADF)
θ
Strong Z dependence as
Rutherford scattering
Scanning Transmission
Electron Microscopy (STEM)
Electron gun
Electromagnetic
lens system
Sample
Annular
Detector
Spectrometer
Fine e- probe ~1Å
e- transparent

5. Understanding heterogeneities of 2D materials at atomic scale
Sub-pm precision measurement
of single-atom vacancy
5 Å
pm
10.0
0.0
-
-
-
5.0
-
-
7.5
2.5
Lee, C.-H., et al. (2020).
Nano Letters, 20(5), 3369-3377.
What are the precise structures
of single-atom defect?
GAN-enhanced realistic
simulated images for ML
1 nm
Input Output
Khan, A., Lee, C.-H., et al. (Under Review)
arXiv: 2301.07743
How to get defect types and
densities at larger scale?
Deep sub-angstrom resolution
imaging of 2D materials
Lee, C.-H., et al. (In preparation)
How does 2D materials look
like at deep sub-Å level?
5 Å
5 / 38

6. Defect-induced strain fields in 2D materials
1D Vacancy Line
Wang, S., et al. (2016) ACS nano
5-7-5-7 to 6-6-6-6 Ring Exchange
Huang, P. Y., et al. (2013) Science Azizi, A., et al. (2017) Nano Letters
Vacancy and
Substitutional defect
However, the measurement precision is still around
8-20 pm, limiting the strain analyses to 2.5% or more.
6 / 38
For WSe2, 1% tensile strain can change the exciton energy by 50 meV.

7. Challenges in probing the atomic defects
ADF-STEM, 80kV, 35pA, dwell 4μs,
15.3 nm x 15.3 nm, 120 frames
5 nm
WSe2-2xTe2x
Single frame dose: 3.9x106 e-/nm2
Total dose: 4.7x108 e-/nm2
Radiation damage is the limiting
factor of achievable SNR, hence
the precision in 2D materials
Challenges
1. Spatial resolution
2. Low signal intensity
3. Detection limit
4. Defect Identification
5. Beam sensitivity
7 / 38

8. Our approach: Class averaging with FCN deep learning
Class averaging
(Rigid-registration)
4. Class-averaged image
SeTe
3 Å
8 / 38
High SNR image is obtained by class-averaging
“equivalent defect sites” identified by deep learning.
Lee, C.-H., et al. (2020). Nano Letters, 20(5), 3369-3377
1. ADF-STEM image
WSe2-2xTe2x
2 nm
• 80kV, aberration-corrected
• α = 25 mrad, probe size 95 pm
• 10-frame averaged, frame time 3 s
• Dwell 2 μs, total dose 107 e-/nm2
2. Identified defect sites
• Fully Convolutional Network (FCN)
• 4 networks for different chalcogen defects
• Trained on simulated STEM images
• Recall, precision, and F1 score all >99%
•
•
•
•
2Te
SeTe
Single Vacancy (Se)
Double Vacancy
Section
“isolated”
defects
Deep
Learning
W
2Se
SeTe
W
2Se
SeTe
2 equivalent
chalcogen sites!
3. Defect images
SeTe sites

9. How does a single Te “squeeze” into WSe2 lattice?
2D Gaussian
fitting
2D Gaussian
fitting
Sum of 437 Defect-free sites
2Se
Sum of 312 Substitution SeTe sites
SeTe
Defect-free 2Se sites Substitution SeTe sites
3 Å
Raw
Precision = 0.3 pm
Class-averaged
2.1 pm
Precision = 5.9 pm
9 / 38
W-W W-W
W-W W-W

10. How SNR and precision scale with number of images?
10 / 38
1 10 100
50 200
SNR =
μ𝑠𝑖𝑔𝑛𝑎𝑙
σ𝑛𝑜𝑖𝑠𝑒
SNR Gain =
𝑆𝑁𝑅𝑠𝑢𝑚𝑚𝑒𝑑
𝑆𝑁𝑅𝑟𝑎𝑤
Precision = σ(𝐴𝑙𝑙 𝑎𝑣𝑔 𝑊 − 𝑊)
3 Å
Images summed with different number of frames (N)
The SNR gain is proportional to 𝑵 and
allows high precision measurement after
class averaging.
SNR determines the precision until the
instrument stability becomes the limiting factor.
∝ ~ 𝑁
∝ ~
𝑃𝑖𝑛𝑖𝑡𝑖𝑎𝑙
𝑁
Precision and SNR gain
Precision ~ 0.27 pm
→ Still limited by number of frames!

11. How different defects distort the lattice?
Class-averaged
DV
SeTe
2Se
2Te
SV
Expansion
Contraction
We measured local distortions of multiple defect types
and observed strong contraction around vacancies.
11 / 38
5 Å
1st NN
5 Å
10
0
-
-
-
5.0 pm
-
-
7.5
2.5
SV
Displacement vectors map
Vectors are enlarged 18x for visibility.

12. 2939
εxx εyy
Dilation
(εxx+ εyy)
Exp
x
y
5 Å
Precision: 0.2 pm 10%
-10%
-
-
-
0%
-
-
5%
-5%
Detecting single vacancy-induced strain
Strain field oscillations induced by single-atom
vacancy are directly visualized and quantified! 12 / 38
Isotropic
Elastic
Continuum
theory
10%
-10%
-
-
-
0%
-
-
5%
-5%
DFT
10%
-10%
-
-
-
0%
-
-
5%
-5%
DFT conducted by Dr. Tatiane P. Santos and Prof. André Schleife

13. How do we precisely measure defect structures?
13 / 38
Oscillating strain field
ADF-STEM image Defect identification
Deep
Learning
Sub-pm precision measurement
Class averaging
2 nm 5 Å 5 Å
pm
10%
-10%
-
-
-
0%
-
-
5%
-5%
10.0
0.0
-
-
-
5.0
-
-
7.5
2.5
2 nm
Deep learning + class-averaging
➡ sub-pm precision

14. Understanding heterogeneities of 2D materials at atomic scale
Sub-pm precision measurement
of single-atom vacancy
5 Å
pm
10.0
0.0
-
-
-
5.0
-
-
7.5
2.5
Lee, C.-H., et al. (2020).
Nano Letters, 20(5), 3369-3377.
What are the precise structures
of single-atom defect?
GAN-enhanced realistic
simulated images for ML
1 nm
Input Output
Khan, A., Lee, C.-H., et al. (Under Review)
arXiv: 2301.07743
How to get defect types and
densities at larger scale?
Deep sub-angstrom resolution
imaging of 2D materials
Lee, C.-H., et al. (In preparation)
How does 2D materials look
like at deep sub-Å level?
5 Å
14 / 38

15. Crystal exfoliation and PL are conducted by Yue Zhang and Dr. Abir M. Hossain
Characterizing atomic defects at micron scale
15 / 38
Large crystals are being exfoliated with higher qualities
but characterizing at such scale remain challenging!
OM
WSe2
PL
Intensity variation?

16. 1. Automated acquisition of
experimental STEM images
Experimental images
CycleGAN
3. CycleGAN training
FCN
5. FCN training
4. CycleGAN
processing
Processed images
6. FCN-identified
defect positions
Defect ground truth
Simulated images
2. Simulate
STEM images
Automatic acquisition with CycleGAN enhancement
16 / 38

17. Connect different applications for automated acquisition
17 / 38
Use python and pywinauto to connect all the
different programs for automated acquisition!
Demonstrated on Thermo Fisher Themis Z S/TEM
Code available on https://github.com/chiahao3/EM-scripts
Python
- Move sample
- Microscope
optics
temscript
- Image
acquisition
Velox
pywinauto
- Auto focus
- Aberration
correction
Sherpa
Scripting
Simulate
mouse action

18. Millions of atom dataset within a single day!
• Custom-built automated acquisition
• 14.4K atoms per image
• Each image is 21✕ 21 nm2
• Acquired 200 images within 9 hrs
– Including drift settling time, beam
shower, low order aberration
correction
• Atomic resolution images of 3M
atoms span across 300✕ 300 nm2
How do we characterize
all these atoms?
18 / 38

19. Larger dataset, larger variation
19 / 38
Larger dataset ≠ Simply longer experiments
Microscope condition changes with time!
SNR for different experiment design
~ 9hrs
Time
Need to include these in the training data!

20. Using CycleGAN to enhance the simulated data
20 / 38
Input simulated image Output exp-like, realistic image
CycleGAN
CycleGAN learns and transfers the experimental feature to the
simulated image, generating high quality training data!
Cycle Generative Adversarial Network
1 nm
Train with Exp. data
Code available on https://github.com/ClarkResearchGroup/stem-learning

21. Which ones are simulated from CycleGAN?
21 / 38
CycleGAN Exp. Exp.
Exp. CycleGAN CycleGAN
1 nm

22. Deep learning-identified intrinsic defects of WSe2
22 / 38
14363 defects (1.3M atoms dataset)
Counts 14135 201 23 4
Counts/atomic
columns
1.62% 0.02% 3E-3% 5E-4%
Percent relative
abundance
98.41 1.40 0.002 0.0003
Area density
(#/cm2)
3.43E13 4.88E11 5.6E10 1E10
SV (VSe) is the most dominant
defect species of WSe2
SV (VSe) VW
SeW
DV (VSe2)
5 Å

23. PL intensity is negatively correlated with the vacancy density
23 / 38
The integrated PL intensity increased by 70%
while vacancy density decreased by 48%
PL integrated intensity map
7M atoms ADF-STEM dataset
OM
Intensity
(a.u.)
PL signal
Photon energy (eV)
Low defect #
High defect #
200 μm
x106

24. How to acquire and analyze million-atom scale data?
24 / 38
Automatic acquisition + CycleGAN
➡ AI-in-the-loop workflow
CycleGAN-enhanced
simulation
1 nm
Input Output
Automated acquisition
Correlating PL map with
million-atom scale ADF-STEM
Robust FCN for
defect identification
200 μm

25. Understanding heterogeneities of 2D materials at atomic scale
25 / 38
Sub-pm precision measurement
of single-atom vacancy
5 Å
pm
10.0
0.0
-
-
-
5.0
-
-
7.5
2.5
Lee, C.-H., et al. (2020).
Nano Letters, 20(5), 3369-3377.
What are the precise structures
of single-atom defect?
GAN-enhanced realistic
simulated images for ML
1 nm
Input Output
Khan, A., Lee, C.-H., et al. (Under Review)
arXiv: 2301.07743
How to get defect types and
densities at larger scale?
Deep sub-angstrom resolution
imaging of 2D materials
Lee, C.-H., et al. (In preparation)
How does 2D materials look
like at deep sub-Å level?
5 Å

26. Comparing ptychography with ADF-STEM
26 / 38
W Se
1 nm
0.95 Å
ADF-STEM
FFT
1 nm
0.41 Å
Ptychography
FFT
0.91 Å
0.67 Å
0.52 Å
2 Å
Line profiles
Ptychography shows much improved
deep sub-angstrom resolution

27. Pixelated
Detector
What is ptychography?
27 / 38
Scanning Transmission
Electron Microscopy (STEM)
Electron gun
Electromagnetic
lens system
Sample
Annular
Detector
4D Scanning Transmission
Electron Microscopy (4D-STEM) Ptychography
Full 2D diffraction
Phase information
Pixelated detector
Not probe-limited
Hours of reconstruction
~ 0.4 Å
4D-STEM
(rx, ry, kx, ky)
ADF-STEM
1 integrated intensity
Amplitude information
Annular detector
Probe-limited resolution
Instant result
~ 1 Å
Conventional STEM
2D scan (rx, ry)
2D diffraction patterns (kx, ky)
Ptychography is a 4D-STEM
technique that utilizes phase retrieval

28. How does ptychography work?
28 / 38
Pixelated
Detector
Electron gun
Electromagnetic
lens system
Sample
2D diffraction patterns (kx, ky)
4D Scanning Transmission
Electron Microscopy (4D-STEM)
𝜓𝑒𝑥𝑖𝑡(𝑘)
2
𝜓𝑒𝑥𝑖𝑡 Ԧ
𝑟 = 𝜓𝑝𝑟𝑜𝑏𝑒 Ԧ
𝑟 ∙ exp 𝑖𝜎𝑉
𝑝
Probe Phase object
Exit wave
Scattering potential
|ℱ 𝜓𝑒𝑥𝑖𝑡 Ԧ
𝑟 |2 = 𝜓𝑒𝑥𝑖𝑡(𝑘)
2
Diffraction pattern
𝜓𝑜(𝑘)
2
𝜓𝑠(𝑘)
2
𝜓𝑜 + 𝜓𝑠
2
This interference term is
probe position dependent!
Real space scan
Central disk
Probe overlaps provide
extra information!

29. Visualizing structural disorder with ptychography
29 / 38
1 nm
• Thermo Fisher Themis Z
• 80 kV, 25.2mrad
• 20 pA, dwell time = 1 ms ( with 0.86 ms integration time)
• Step scan size = 0.4 Å
• Scan size = 51 x 51 Å 2 (128 x 128)
• Maximum-likelihood, 10 probe modes, 21K iterations
• Pixel size = 9.8 pm
• https://github.com/yijiang1/fold_slice
0.408 Ang
Cropped FFT
0.41 Å
Ptychography
0.41 Å
Line profile of FFT
Spatial frequencies (Å -1)
Log
of
power
(a.u.)
AC-STEM 0.95 Å
Monolayer WSe2

30. Atomic defects are easily identified
30 / 38
Monolayer WSe2
1 nm
Double vacancy Single vacancy
Atomic defects are easily identified
and distinguishable with the improved
resolution and SNR
5 Å 5 Å

31. Non-uniform projected W-W distances
31 / 38
The histogram shows multiple
peaks with contributions from:
1. defect-induced strain
2. tilted projection
Projected W-W distance
3.6
2.8
3.4
3.2 Å
3.0
1 nm

32. Strain field oscillation of single vacancies
32 / 38
Ptychography
Single acquisition
Ptychography reproduced
the oscillating strain field
with a single data set
Class averaging
DL identified 3000 frames
5 Å
Precision: 0.2 pm
10%
-10%
-
-
-
0%
-
-
5%
-5%
Projected
dilation
Expansion
Contraction
1 nm
Expansion
Contraction
10 %
-15 %
5 %
-5 %
-10 %
0 %
15 %
Projected
dilation

33. Local tilting induced Se-Se separation
33 / 38
W
Se-Se
Local tilting can be derived from the
projected Se-Se separation (Δ𝑥 = 𝑑 sin𝜃)
Se
W
Projected displacement Δ x
d
Chalcogen atom distance
Tilt angle θ
Slightly tilted top-down view
2 Å
Δ x ~ 0.8 Å
(~ 14o)

34. Reconstructed surface rippling of 2D materials
Surface rippling resolved by electron ptychography!
34 / 38

35. How does 2D materials look like at deep sub-Å level?
35 / 38
Ptychography
➡ sub-Å resolution, pm precision
structural characterization
Reconstructing 3D
surface rippling
Deep sub-angstrom resolution
imaging of 2D materials
5 Å 1 nm
Projected
dilation
Expansion
Contraction
10 %
-15 %
5 %
-5 %
-10 %
0 %
15 %
Visualizing defect-induced
lattice distortion

36. Implications and broader impacts
36 / 38
Sub-pm precision measurement
of single-atom vacancy
5 Å
pm
10.0
0.0
-
-
-
5.0
-
-
7.5
2.5
Lee, C.-H., et al. (2020).
Nano Letters, 20(5), 3369-3377.
What are the precise structures
of single-atom defect?
GAN-enhanced realistic
simulated images for ML
1 nm
Input Output
Khan, A., Lee, C.-H., et al. (Under Review)
arXiv: 2301.07743
How to get defect types and
densities at larger scale?
Deep sub-angstrom resolution
imaging of 2D materials
Lee, C.-H., et al. (In preparation)
How does 2D materials look
like at deep sub-Å level?
5 Å

37. Displacement vectors Dilation (εxx+ εyy)
5 Å
Vectors are enlarged 10x for visibility.
10%
-10%
-
-
-
0%
-
-
5%
-5%
Future Directions: Precise defect structures
37 / 38
1.Defect complexes for catalysts, qubits, and quantum emitters
2.Defect-defect interaction
3.3D structure of interfaces and grain boundaries
4.Resolving anisotropic atomic vibration
σa
σb
𝝈𝒂
𝝈𝒃
1.2
1.0
1.1
Atom ellipticity
Defect position

38. Future Directions: AI in microscopy
38 / 38
Generated by Bing Image Creator
Futuristic man-like robot operating
a tall electron microscope.
1.Large scale correlative microscopy analysis
2.Self-driving microscopes
3.AI-accelerated analysis of high-dimensional data
4.AI-accelerated ptychographic reconstruction
5 Å
Hours to days
~mins ?
4D-STEM data set
Ptychographic
reconstruction
Bayesian optimization,
Generative models

39. Thanks for your attention!