hgcdte noise from µhz to khz roger smith, gustavo rahmer, david hale, elliott koch caltech...
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HgCdTe Noise from µHz to kHz
Roger Smith,
Gustavo Rahmer, David Hale, Elliott Koch
Caltech
Detectors for AstronomyGarching, 2009-10-14
Garching, 2009-10-14HgCdTe Noise from µHz to kHz
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Conclusion
In theory, there is no difference
between theory and practice,
but, in practice, there is. Jan L. A. van de Snepscheut or Yogi Berra ?
Measure it the way you will use it.
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Noise studies in progress
• We are particularly interested in the noise floor where many samples are combined.
• At the extremes of exposure time, are the causes for the noise floor the same ?
Applications Exp. t Window Processing
Wavefront Sensing millisec 4x4 Extreme Fowler
Imaging minutes Full frame Moderate Fowler
Spectroscopy hours 300x500 Extreme, least squares fit
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First, optimize pixel timing
• 10 µs/pixel is standard. We had 3µs dwell.• We reduced overheads to 2.16µs, and overlapped this with signal settling.• For 3µs dwell, pixel time is halved: sample twice as often with same noise bandwidth.
For Astronomical Research Cameras Inc. 8ch IR video card
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Settle and Dwell Time Optimization6
Will Signal-to-Noise ratio be improved more by:– increasing settling time above 2µs, or
– adding more dwell time (noise BW limiting), or
– coadding more frames ?
More coadds are better than more settling.
More dwell is better at high frequency, with most gain by 4us; slightly worse at low frequency.
6µs/pixel is good compromise.
Small window for fast readout
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Possible causes of noise floor ?
• Dark currentIdark < 0.002 e-/s for 1.7µm @120K, < 0.004 e-/s for 2.5µm @80K.
• Mux glow ?Iglow < 0.0034 e-/read for 5µs/pixel.
Keep sample rate << 1.7s/read, so Iglow << Idark
• 1/f noise in detector material.
• RTS noise in mux (on small number of pixels)
• Bias variationsStabilize biases; remove common mode with ref pixels.
• Thermal variationsGood temperature control (~0.8e-/mK)Constant cadence clocking for uniform self heating.Could use metal trace on mux to track its temperature better then apply correction based on per pixel temperature coefficient.
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Dark signal … Is this mux glow? 9
For SUR at 2s/sample, Idark = 0.008 e-/s
For small fast windows, 0.0034e-/read at 6µs/pixel
Frame number Time (s)
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Self-heating masquerades as mux glow
As window size is reduced same power is concentrated in smaller area so temperature rises: dark current increases with number of reads rather like mux glow.
8x8 windowAfter160,000 frame SUR in 75s
32x32 windowAfter 10,000 frame SUR in 75s
8x8 Hot spot in next readout
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Spatial variation in noise
Noise histogram has high tail.
Why worry?..
• Wavefront sensing: don’t want small guide window to land on a bad pixel.
• Spectrocopy: don’t want key spectral feature on a bad pixel.
RTS noise in mux?
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Raw pixel values vs Time (no coadding)
• Noisiest pixels exhibit “Random Telegraph Signal” a bimodal noise distribution due to single traps in or channel near buffer FET.
• Number of such traps and distance from channel produce a spectrum of amplitudes.
• Characteristic time constants vary widely.
• All silicon transistors suffer from this to some extent. In big transistors many traps are in play and it accounts for 1/f noise. In small transistors one or a few traps produce RTS noise.
• Cooling increases the time constant. Slow traps become so slow they become invisible, but fast traps which would average to zero now move into signal passband.
Quiet pixel
Excess noise is due to RTS in mux
Raw
val
ue m
inus
1st
fram
e (A
DU
)
Frame number
Noisy pixel
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Histogram of RTS noisefor the nasty case of two traps about the same size
Time series
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Same after coadd and subtract (100 coadds)
• For time series on previous slide
• Differencing turns steps into spikes.
• Coadding helps but noise is still
• Better to reject outliers than try to average them away
Garching, 2009-10-14HgCdTe Noise from µHz to kHz
16Spatial distribution of Noisedifferent processing of same data in each case
Fowler 1 Fowler 16 Fowler 256
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Noise vs exposure time
Same SUR data in both cases:
• For CDS use samples n sec apart.
• For CDS sum or n sec, then subtract from sum of next n sec.
Maximum number of fowler samples at 0.5Hz fitting into each exposure time, for this data point fowler 50.
Fowler 5
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Power Spectral Distribution
From SUR data used in previous slide
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Raw, CDS with alternate samples
SUR at 0.5Hz:
CDS frames synthesized from alternate samples
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Raw, CDS with alternate samples
SUR at 0.5Hz:
CDS frames synthesized every 2nd sample.
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Noise vs frame rate for small windows, deep sampling
Turn up due to dark current + mux glow
Latest low noise 2.5µm recipe
Frame rate after fowler sampling
Noise floor due to 1/f noise.
Kink
Fixed by excluding hot pixels not present in smaller windows
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Power spectra vs Sample rate26
1/f noise causes floor at low frequencies
If noise power spectrum is a property of the detector, why does the 1/f corner and white noise floor change with SUR sample rate (window size) ?
Nyquist ~ 2.1kHz 1/f corner ~ 3.5Hz
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PSD, sampling at 0.5Hz
Frequency range for previous slide
Nyquist =0.25 Hz
1/f corner = 0.0035Hz
Isn’t the power spectrum a property of the detector?
How can it change with sample rate ?
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Aliasing “101”P
ower
Den
sity
Sample rate/2
Sample rate
Sample rate*3/2
BW ~ 1/pixel time
~ 1/ frame time
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Aliasing “101”P
ower
Den
sity
Sample rate/2
Sample rate
Sample rate*3/2
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Simulated Aliasing of 1/f + white noise
White noise above nyquist shows up in baseband due to aliasing.
Nyquist frequency
No aliasing
Elevated noise floor due to aliases
With alias
Without alias
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Aliasing of pure 1/f noise
Even pure 1/f looks like it has a white noise floor after aliasing.
Nyquist frequency
No aliasing
Flattening due to aliases
With aliasing
Without
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Why does 1/f corner move ?
• Noise BW ~ 2/pixel_T– For CCD, sample rate = 1/pixel_T
– For mulitplexed detector, sample rate = 1/frame_T
……most of the noise BW is above Nyquist.
• White noise floor is raised by aliasing …illustrated in next slides … This lowers the 1/f corner.
This explains how fowler sampling can still work even when one expects 1/f noise to dominate.
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Conclusion
In theory, there is no difference
between theory and practice,
but, in practice, there is. Jan L. A. van de Snepscheut or Yogi Berra ?
Measure it the way you will use it.
PS: Data comparing noise spectra for 1.7µm and 2.5µm materials will be submitted on the web site.