aboutsummaryrefslogtreecommitdiff
path: root/presentation.tex
blob: 9d0e3b9e7497c5ab52a67dd9598cea56ab4ed74a (plain)
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
242
243
244
245
246
247
248
249
250
251
252
253
254
255
256
257
258
259
260
261
262
263
264
265
266
267
268
269
270
271
272
273
274
275
276
277
278
279
280
281
282
283
284
285
286
287
288
289
290
291
292
293
294
295
296
297
298
299
300
301
302
303
304
305
306
307
308
309
310
311
312
313
314
315
316
317
318
319
320
321
322
323
324
325
326
327
328
329
330
331
332
333
334
335
336
337
338
339
340
341
342
343
344
345
346
347
348
349
350
351
352
353
354
355
356
357
358
359
360
361
362
363
364
365
366
367
368
369
370
371
372
373
374
375
376
377
378
379
380
381
382
383
384
385
386
387
388
389
390
391
392
393
394
395
396
397
398
399
400
401
402
403
404
405
406
407
408
409
410
411
412
413
414
415
416
417
418
419
420
421
422
423
424
425
426
427
428
429
430
431
432
433
434
435
436
437
438
439
440
441
442
443
444
445
446
447
448
449
450
451
452
453
454
455
456
457
458
459
460
461
462
463
464
465
466
467
468
469
470
471
472
473
474
475
476
477
478
479
480
481
482
483
484
485
486
487
488
489
490
491
492
493
494
495
496
497
498
499
500
501
502
503
504
505
506
507
508
509
510
511
512
513
514
515
516
517
518
519
520
521
522
523
524
\documentclass{presentation}

\title{Development of \\ Frequency-Domain Multidimensional Spectroscopy}
\subtitle{---Beyond Two Dimensions---}
\author{Blaise Thompson}

\institute{University of Wisconsin--Madison}
\date{2018-04-23}

\begin{document}
\maketitle

\begin{frame}{Introduction to CMDS}
  \begin{columns}
    \begin{column}{0.6\textwidth}
      \adjincludegraphics[scale=0.2]{presentation/SK_PhDThesis_fsTable-Overview} \\
      \tiny
      Figure courtesy of Schuyler Kain
    \end{column}
    \begin{column}{0.4\textwidth}
      \includegraphics[width=\textwidth]{"literature/BrownEmilyJ1999a_1"}
      \centering
      \\
      \vspace{2\baselineskip}
      $\vec{k_{\text{sig}}} = \vec{k_a} - \vec{k_b} + \vec{k_c}$
      \vspace{2\baselineskip} \\
      \tiny \raggedright
      Figure: \\
      Brown, E., Zhang, Q. and Dantus, M. (1999). \\
      The Journal of Chemical Physics, 110(12), pp.5772-5788.
    \end{column}
  \end{columns}
\end{frame}

\begin{frame}{Introduction to CMDS: microscopic picture}
  \adjincludegraphics[width=\textwidth]{"mixed_domain/simulation overview"}
\end{frame}

\begin{frame}{Diversity}
  Great diversity of experimental strategies under the ``umbrella'' of CMDS:
  \vspace{\baselineskip} \\
  \begin{columns}
    \begin{column}{0.5\textwidth}
      Experimental geometry...
      \begin{itemize}
        \item $\vec{k_a} - \vec{k_b} + \vec{k_c}$
        \item $\vec{k_a} + \vec{k_b} + \vec{k_c}$
        \item $\vec{k_a} - \vec{k_a} + \vec{k_b} + \vec{k_c} + \vec{k_d}$
      \end{itemize}
    \end{column}
    \begin{column}{0.5\textwidth}
      Dimensions explored...
      \begin{itemize}
        \item MIR \& visible: DOVE, TRSF
        \item fully visible: TREE, CARS
        \item frequency-frequency: 2DES/2DIR, ``Resonant-(Raman/IR)''
        \item frequency-delay: TG, TA
        \item delay-delay: 3PE, MUPPETS
      \end{itemize}
    \end{column}
  \end{columns}
  \vphantom{m} \\
  Or 3D.. or 4D: many possibilities not yet popular enough to name  
  % SAY: based on the same basic ability to scan pulses in frequency, delay etc
  %   all possible with one instrument
  %   each has been focus of entire careers...
\end{frame}

\begin{frame}{My focus}
  Focus on the \emph{pipeline} of CMDS:
  \begin{itemize}
    \item throughput
    \item quality
    \item diversity
  \end{itemize}
%   \vphantom{M} \\
%   Unlock the true potential of these instruments:
%   \begin{itemize}
%     \item automated calibration
%     \item 2D, 3D, 4D...
%     \item full diversity of possible hardware combinations, rapid development
%     \item powerful and flexible detection strategies
%     \item data processing tools
%   \end{itemize}
\end{frame}

\section{Tunability}  % ===========================================================================

\begin{frame}{Tunability}
  \centering \huge
  Control and Calibration of \\
  Optical Parametric Amplifiers
\end{frame}

\begin{frame}{Two strategies for CMDS}
  \begin{columns}
    \begin{column}{0.5\textwidth}
      Time Domain
      \includegraphics[width=\textwidth]{"literature/SinghAkshay2014a_2"}
      \tiny
      Figure: \\
      Singh, A., Moody, G., Wu, S., Wu, Y., Ghimire, N., Yan, J., Mandrus, D., Xu, X. and Li, X.
      (2014).
      Coherent Electronic Coupling in Atomically Thin MoSe$_2$. Physical Review Letters, 112(21).
    \end{column}
    \begin{column}{0.5\textwidth}
      Frequency Domain
      \adjincludegraphics[width=\textwidth]{presentation/singh_czech}
      More \hl{bandwidth}.
      Crucial for electronic states, band structure.
    \end{column}
  \end{columns}
  % SAY:
  % - time domain: broadband, interferometric, fast
  % - frequency domain: narrowband, direct, slow but broad bandwidth
  % - bandwidth crucial for exploring diverse states
\end{frame}

\begin{frame}{Bandwidth}
  A lot more bandwidth... through the usage of OPAs
  \adjincludegraphics[width=\textwidth]{opa/OPA_ranges}
  \phantom{M} \hfill but how to make this strategy easy and \hl{robust}?
  % SAY: previous spectrum was SHS
  % SAY: crystal angle, temporal and spatial overlap
\end{frame}

\begin{frame}{Automation}
  \begin{columns}
    \begin{column}{0.5\textwidth}
      \adjincludegraphics[width=\textwidth]{opa/autotune_preamp}
    \end{column}
    \begin{column}{0.5\textwidth}
      Fully automated OPA tuning
      \begin{itemize}
        \item less than 1 hour per OPA
        \item can be scheduled during down time
        \item high quality from global analysis 
        \item reproducible
        \item unambiguous representations automatically generated to assess health
      \end{itemize}
      \vspace{\baselineskip}
      Other calibration also needed, automated.
    \end{column}
  \end{columns}
  % SAY: one example for one stage in one model of OPA (out of four in WG)
\end{frame}

\section{Acquisition}  % ==========================================================================

\begin{frame}{Acquisition}
  \centering \huge
  Control of the MR-CMDS \\
  Instrument
\end{frame}

\begin{frame}{The instrument}
  Many kinds of component hardware
  \begin{itemize}
    \item monochromators
    \item delay stages
    \item filters
    \item OPAs
  \end{itemize}
  $\sim10$ settable devices, $\sim25$ motors, multiple detectors. \\
  \vphantom{M} \\
  \hl{Coordination} problem.
\end{frame}

\begin{frame}{Acquisition}
  PyCMDS---unified software for controlling hardware and collecting data.
  \adjincludegraphics[width=\textwidth]{acquisition/screenshots/000}
\end{frame}

\begin{frame}{Acquisition}
  Capabilities of PyCMDS:
  \begin{itemize}
    \item reconfigures itself based on available hardware (modularity)
    \item multithreaded (up to 2x speed enhancement)
    \item queued acquisitions
      \begin{itemize}
        \item long scans, short window of calibration---large duty cycle needed
        \item shortly after implementation, two weeks of data collection yielded as many pixels as
          the previous three years
      \end{itemize}
    \item extensibility
      \begin{itemize}
        \item easy to add new hardware, new sensors, and new acquisition strategies
        \item typical addition $\sim100$ lines of new Python code
      \end{itemize}
  \end{itemize}
\end{frame}

\begin{frame}{Extensibility}
  \begin{columns}
    \begin{column}{0.25\textwidth}
      \adjincludegraphics[width=\textwidth]{presentation/hardware}
    \end{column}
    \begin{column}{0.7\textwidth}
      Easy to add new hardware to PyCMDS
      \begin{itemize}
        \item In 2016, a new OPA was added to the picosecond system in one day.
        \item In 2017, we added multiple delay stages to the femtosecond system. Implementation
          took between one and four hours.
      \end{itemize}
      Once added, new hardware is immediately available for scanning in a multidimensional space
      with other hardware---no additional programming needed!
    \end{column}
  \end{columns}
\end{frame}

\section{Measurement enhancements}  % =============================================================

\begin{frame}{Measurement enhancements}
  \centering \huge
  Measurement enhancements
\end{frame}

\begin{frame}{Digital processing}
  \large
  boxcar averager \hfill digitize immediately
  \begin{columns}
    \begin{column}{0.5\textwidth}
      \adjincludegraphics[width=\textwidth]{presentation/IMG_20180422_145632}
    \end{column}
    \begin{column}{0.5\textwidth}
      \adjincludegraphics[width=\textwidth]{presentation/measure_digitize}
    \end{column}
  \end{columns}
  \vphantom{M} \\
  \begin{itemize}
    \item cheaper, fewer points of failure
    \item more flexibility for different detector configurations
    \item shot-level statistics, processing sequences
    \begin{itemize}
      \item configurable through simple python script
    \end{itemize}
    \item $\sim3\times$ faster
  \end{itemize}
\end{frame}

\begin{frame}{Dual chopping}
  \begin{columns}
    \begin{column}{0.5\textwidth}
      \adjincludegraphics[width=\textwidth]{presentation/IMG_20180422_145757}
    \end{column}
    \begin{column}{0.5\textwidth}
      \begin{tabular}{ r | c | c | c | c }
        & A       & B          & C          & D                       \\
        signal    &            &            & \checkmark &            \\
        scatter 1 &            & \checkmark & \checkmark &            \\
        scatter 2 &            &            & \checkmark & \checkmark \\
        other     & \checkmark & \checkmark & \checkmark & \checkmark
      \end{tabular}
    \end{column}
  \end{columns}
  \centering
  \vphantom{M}
  $\mathsf{I_{signal} = A - B + C - D}$ \\
  \phantom{M} \\
  \raggedright
  Isolate signal that depends on \emph{all} indecent beams.
  \begin{itemize}
    \item{no scatter}
    \item{no competing signals}  % e.g. 2k1+k3 vs k1+k2+k3
    \item{no voltage offset or room lights}
  \end{itemize}
\end{frame}

\begin{frame}{Digital processing}
  \adjincludegraphics[width=\textwidth]{presentation/chopped}
  % SAY: this is a GOOD example
  % - we now regularly extract signals that would be completely invisible on the 'detected' side
\end{frame}

\section{Processing}  % ===========================================================================

\begin{frame}{Processing}
  \centering \huge
  Data processing
\end{frame}

\begin{frame}{Processing}
  \begin{columns}
    \begin{column}{0.6\textwidth}
      Great! We have \emph{lots} of CMDS data. \\
      Now what? \\
      \vphantom{M} \\
      Working with multidimensional data is hard...
      \begin{itemize}
        \item storage
        \item visualization
        \item post-processing
        \item fitting, modeling
      \end{itemize}
      and the dimensions are always changing! \\
      \vphantom{M} \\
      WrightTools---software to process CMDS.
    \end{column}
    \begin{column}{0.4\textwidth}
      \adjincludegraphics[width=\textwidth]{presentation/TOC}
    \end{column}
  \end{columns}
  % SAY: multidimensional includes > 2 dimensions
\end{frame}

\subsection{Universal format}  % ------------------------------------------------------------------

\begin{frame}{Universal format}
  WrightTools defines a \emph{universal file format} for CMDS.
  \begin{itemize}
    \item store multiple multidimensional arrays
    \item metadata
  \end{itemize}
  Import data from a variety of sources.
  \begin{itemize}
    \item previous Wright Group acquisition software
    \item commercial instruments (JASCO, Shimadzu, Ocean Optics)
    \item simulation packages
  \end{itemize}
\end{frame}

\subsection{Flexible data model}  % ---------------------------------------------------------------

\begin{frame}{Flexible data model}
  Flexibility to transform into any desired ``projection'' on component variables.
  \adjincludegraphics[width=\textwidth]{processing/fringes_transform}
  % SAY: 'derived dimensionality' - DJM
  % - including expressions
\end{frame}

\subsection{Integrations}  % ----------------------------------------------------------------------

\begin{frame}{Integrations}
  \begin{columns}
    \begin{column}{0.6\textwidth}
      \adjincludegraphics[width=\textwidth]{PEDOT_PSS/agreement}
    \end{column}
    \begin{column}{0.4\textwidth}
      \begin{itemize}
        \item WrightTools as a backend
        \item puts models and experiments on the same footing
        \item makes custom modeling work easier
        \item more general-purpose modeling coming soon
      \end{itemize}
    \end{column}
  \end{columns}
\end{frame}

\section{Conclusion}  % ===========================================================================

\begin{frame}{Conclusion}
  \begin{columns}
    \begin{column}{0.5\textwidth}
      \adjincludegraphics[height=1.5in]{opa/autotune_preamp}
      \adjincludegraphics[width=\textwidth]{acquisition/screenshots/000}
    \end{column}
    \begin{column}{0.5\textwidth}
      \adjincludegraphics[width=\textwidth]{presentation/chopped}
      \phantom{M}
      \adjincludegraphics[width=\textwidth]{processing/fringes_transform}
    \end{column}
  \end{columns}
\end{frame}

\begin{frame}{Acknowledgments}
  \begin{columns}
    \begin{column}{0.5\textwidth}
      Wright Group
      \begin{itemize}
        \item Kyle Sunden
        \item Natalia Spitha
        \item Darien Morrow
        \item Jonathan Handali
        \item Nathan Neff-Mallon
        \item Kyle Czech         
        \item Dan Kohler
        \item Erin Boyle
        \item Paul Hebert
        \item Skye Kain
        \item John
        \item (and more...)
      \end{itemize}
    \end{column}
    \begin{column}{0.5\textwidth}
      Committee
      \begin{itemize}
        \item Kyoung-Shin Choi
        \item Randall Goldsmith
        \item Tim Bertram
      \end{itemize}
      \phantom{M}
      UW-Madison Chemistry Department
      \begin{itemize}
        \item Rob McClain
        \item Pam Doolittle
        \item Bill Goebel
        \item Steve Myers
      \end{itemize}
      \phantom{M}
      Friends and family \\
      \phantom{M} \\
      You, the audience!
      \hl{Questions?}
    \end{column}
  \end{columns}
\end{frame}
  
\section{Supplement}  % ===========================================================================

\begin{frame}{TOPAS-C}
  One of four models of OPAs used within the Wright Group.
  \includegraphics[width=\textwidth]{opa/TOPAS-C}
  Two ``stages'', each with two motorized optics.
\end{frame}

\begin{frame}{Two strategies for CMDS}
  Two strategies for collecting multidimensional spectra:
  \vspace{\baselineskip} \\
  \begin{columns}
    \begin{column}{0.4\textwidth}
      Time Domain
      \begin{itemize}
        \item broadband pulses
        \item resolve spectra interferometrically
        \item fast (even single shot)
        \item robust
      \end{itemize} 
    \end{column}
    \begin{column}{0.4\textwidth}
      Frequency Domain
      \begin{itemize}
        \item narrowband pulses
        \item resolve spectra by tuning OPAs directly
        \item slow (lots of motor motion)
        \item fragile
      \end{itemize}
    \end{column}
  \end{columns}
\end{frame}

\begin{frame}{Tuning}
  % TODO: curve plot?
  Tuning curves---recorded correspondence between motor positions and output color.
  \vspace{\baselineskip} \\
  Exquisite sensitivity to alignment and lab conditions---tuning required roughly once a week.
  \vspace{\baselineskip} \\
  Manual tuning is difficult...
  \begin{itemize}
    \item high dimensional motor space
    \item difficult to asses overall quality
    \item several hours of work per OPA (sometimes, an entire day for one OPA)
  \end{itemize}
\end{frame}

\begin{frame}{Preamp}
  \includegraphics[width=\textwidth]{opa/preamp}
\end{frame}

\begin{frame}{Modular hardware model}
  \adjincludegraphics[scale=0.25]{acquisition/hardware_inheritance}
\end{frame}

\begin{frame}{Modular sensor model}
  Can have as many sensors as needed.
  \vspace{\baselineskip} \\
  Each sensor contributes one or more channels.
  \vspace{\baselineskip} \\
  Sensors with size contribute new variables (dimensions).
\end{frame}

\begin{frame}{Domains of CMDS}
  CMDS can be collected in two domains:
  \begin{itemize}
    \item time domain
    \item frequency domain
  \end{itemize}
\end{frame}

\begin{frame}{Time domain}
  Multiple broadband pulses are scanned in \emph{time} to collect a multidimensional interferogram
  (analogous to FTIR, NMR).
  \vspace{\baselineskip} \\
  A local oscillator must be used to measure the \emph{phase} of the output.
  \vspace{\baselineskip} \\
  This technique is...
  \begin{itemize}
    \item fast (even single shot)
    \item robust
  \end{itemize}
  pulse shapers have made time-domain CMDS (2DIR) almost routine.
\end{frame}

\begin{frame}{Frequency domain}
  In the Wright Group, we focus on \emph{frequency} domain ``Multi-Resonant'' (MR)-CMDS.
  \vspace{\baselineskip} \\
  Automated Optical Parametric Amplifiers (OPAs) are used to produce relatively narrow-band pulses.
  Multidimensional spectra are collected ``directly'' by scanning OPAs against each-other.
  \vspace{\baselineskip} \\
  This strategy is...
  \begin{itemize}
    \item slow (must directly visit each pixel)
    \item fragile (many crucial moving pieces)
  \end{itemize}
  but! It is incredibly flexible.  
\end{frame}

\begin{frame}{Selection rules}
  MR-CMDS can easily collect data without an external local oscillator.
  \vspace{\baselineskip} \\
  This means... [BOYLE]
\end{frame}

\begin{frame}{Central loop}
  At its core, PyCMDS does something very simple...
  \vspace{\baselineskip} \\ 
  Set, wait, read, wait, repeat.  % TODO: better figure
  \vspace{\baselineskip} \\
  Everything is multi-threaded (simultaneous motion, simultaneous read).
  \begin{itemize}
    \item decrease scan time by up to $\sim2\times$, more for complex experiments
  \end{itemize}
\end{frame}

\end{document}