A Segmented Chirped-Pulse Fourier
Transform Millimeter Wave
Spectrometer (260-290 GHz) with
Real-time Signal Averaging Capability
Brent J. Harris, Amanda L. Steber, and Brooks H.Pate
Department of Chemistry
University of Virginia
Segmented CP-FT: Single Spectrum Acquired in 100 ms
Simple Benchtop Prototype
100 µs, 35 GHz (260 – 295 GHz)
65 cm sample cell
Advantages for Chirped Pulse mm-wave Spectroscopy
1) Rapid spectrum acquisition (35 GHz spectrum acquired in 2 ms)
2) Coherent measurement methods for spectrum analysis
3) Transition frequency accuracy
4) Simple spectrum signal processing (no baseline effects)
Challenges for Chirped Pulse mm-wave Spectroscopy
1) Real-time signal averaging to achieve high sensitivity
2) Reduction of spurious signals
3) Spectral resolution and Fourier transform signal processing
A. Steber, B. Harris, J. Neill, B. Pate, J. Mol. Spec 280 (2012) 3-10
Instrument Design Principles: The Chirped Pulse
1) The Chirped Pulsed Separates the Pulse Duration from the Pulse Bandwidth
High-Resolution Spectroscopy: Line width is much smaller than frequency range
Line width: set by the signal dephasing (Doppler, Collisional Relaxation)
Optimal Excitation Pulse Duration: ~ 1 / (line width)
Transform Limited Pulse Duration: ~ 1 / (frequency range)
2) In the Weak Pulse Limit the Signal is proportional to (Bandwidth)-1/2
Signals for transform limited pulses scale
with (Bandwidth)-1 :
Spectral energy density reduced by both
increased bandwidth and shorter pulse
duration.
Instrument Design Principles: Segmenting
Segmenting the Measurement Frequency Range
Full Bandwidth CP-FT
Rel .Signal in Segment: (2)½
Reduction in Averages: ½
Rel. Measurement Time: 2 * ½ = 1
Rel. Signal in Segment: (N)½
Reduction in Averages: (1/N)
Rel. Measurement Time: N * (1/N) = 1
Relative Segment Bandwidth: (1/N)
(Transform limited pulses: increasing the
segment bandwidth increases the
measurement time!)
Things that are segment independent (all things being equal):
1) The time required to reach a target dynamic range (signal-to-noise ratio)
2) The number of points that make up the spectrum
For smaller segments, lower digitizer rates can be used
Instrument Design Principles: Segmenting
Segmenting the Measurement Frequency Range
Things that are segment dependent (all things being equal):
1) The time required for one spectrum scan (N times longer)
2) The total number of data points processed (Reduced by N)
Molecules perform some of the averaging
Things that are not equal:
1) Digitizer cost and real-time signal averaging performance (favors more segments)
Still need bandwidth to use power efficiently and minimizing stitching
2) Proliferation of spurious signals (favors more segments)
Exception: Single Segment has best mm-wave practical spur performance
260-295 GHz CP-FT Design for Real Time Averaging
Number of Spectrum Points:
35 GHz frequency range, ~ 2ms dephasing time
Minimum Points: (70 Gs/s) x (2 ms) = 140,000
This number of data points can be accommodated in FPGA accumulator
512K points maximum, 8-bit digitizer, 32-bit data size
Maximum real-time accumulations: 2(32-8) = 224 = 16M
Minimizing the Spurious Signals:
Strongly Dependent on AWG purity for LO generation
LO Impurity Spurs: LO-to-IF Conversion (self mixing): Easily subtracted
Spectrum Images: Improved AWG performance on N*30 MHz
This fixes the segment bandwidth to 30MHz x 24 = 720 MHz: 50 segments, 100 ms scan time
Digitizer/Receiver Spurs: Second Order IM: work in 2nd Nyquist zone (720 – 1440 MHz)
Third Order (Two-tone IM)
Signal/Clock Mixing in Digitizer
Segmented Chirped-Pulse Fourier Transform Spectroscopy
Low IF (720 – 1440 MHz)
10 MHz Rb
Standard
2.5 GS/s
4GS
Digitizer
Digitizer
x12 Multiplier
Chain
Sub-harmonic mixer
12 GS/s
AWG
220 – 325 GHz
WR 3.4
8.80 GHz
PDRO
2-3.5 GHz
10.8-12.3 GHz
x24 Multiplier
Chain
Chamber
260– 295 GHz
Output Power: 30-40 mW
Separate AWG Channels Generate Chirp
Segments (Blue)
and
Local Oscillator (LO) Frequency (Red)
with Phase Reproducibility
High Speed Segmented CP-FT Measurement
Digitized at an IF of 720 –
1440 MHz at 4GS/s
Real-Time signal averaging
in 32 bit FPGA :
524288 sample memory
(131 µs, at 4GS/s)
2 µs segments:
- 0.250 µs chirped pulse
- 1.75 µs decay time
- 720 MHz bandwidth
(per segment)
- 50 segments
(36 GHz, 100 µs )
Spectrum Relative Intensity Performance
Frequency dependent
spectrometer response:
- Source power
- Receiver mixer loss
- IF amplifier gain
- Propagation loss
System response correction:
- Routine response curve
generated with series of
stored single frequency
waveforms
- Correction incorporated into
signal processing
About 10-15% accuracy
Intensity Reproducibility
Intensity variation < 0.1%
Suitable for monitoring
time evolution of the
broadband spectrum
Comparison to Literature mm-wave Benchmark
S. Fortman, I. Medvedev, C. Neese, F. De Lucia, ApJ, 725 (2010) 1682
CH3CH2CN 0.5 mTorr, 6 m path length, 24 s equivalent measurement time
Speed: CP-FT is 10,000 times faster
Intensity Accuracy: 15% vs. 1% (but 0.1% precision)
Line width: 2.5 broader in CP-FT (magnitude FT + windowing)
High Dynamic Range Mode (HDR)
HDR sequence:
-
0.250 µs chirped pulse
1.75 µs decay time
24 MHz bandwidth/seg
30 segments for each LO
frequency (720mHz)
- 31st segment for electrical
background subtraction
- 50 LO frequencies (1500
total segments)
62 ms for 720 MHz
Two 720 MHz segments can be
accommodated in the FPGA
Noise Floor Achieved in 1s: 1 mV (60,000:1 dynamic range)
Broadband Double Resonance Spectroscopy
Measurement
Protocol:
Acrolein
1) Single Frequency
Pump Pulse
2) 720 MHz Chirp
Observe ~60% signal
modulation
Sequential and VType Level Schemes
Show Different
Modulation Behavior
Conclusions
1) Real-time Signal Averaging is Achieved using Segmented Chirped Pulse
Fourier Transform Spectroscopy
2) High Speed Mode Significantly Reduces Measurement Times Over
Absorption Spectroscopy Methods
3) A High Dynamic Range Mode Provides Spurious Signal Reduction and
Achieves about 100,000:1 (Largely) Spur Free Performance
4) Capabilities for Broadband Double Resonance Spectroscopy Can Translate
Reduced Spectrum Acquisition Times to Reduced Spectrum Analysis Times
Acknowledgments
Brent Harris is supported by an NSF Graduate Fellowship
University of Virginia Equipment Trust Fund
NSF I-Corps