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Ultrashort laser pulse characterization using modified spectrum auto-interferometric correlation (MOSAIC)

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Ultrashort laser pulse characterization using modified spectrum auto-interferometric correlation (MOSAIC)
Ultrashort laser pulse characterization using
modified spectrum auto-interferometric
correlation (MOSAIC)
Daniel A. Bender,1* Jeffrey W. Nicholson,2
and Mansoor Sheik-Bahae1
1
Department of Physics and Astronomy, Optical Science and Engineering, University of New Mexico, 800 Yale Blvd.
NE, Albuquerque, NM 87131, USA
2
OFS Labs, 19 Schoolhouse Road, Suite 105, Somerset, NJ 08873, USA
*
Corresponding author: [email protected]
Abstract: Sensitive, real-time chirp and spectral phase diagnostics along
with full field reconstruction of femtosecond laser pulses are performed
using a single rapid-scan interferometric autocorrelator. Through the use of
phase retrieval error maps, ambiguities in pulse retrievals based on the pulse
spectrum and various forms of MOSAIC traces are discussed. We show
second-order autocorrelations can introduce significantly different amounts
of chirp depending on the implementation. Examples are presented that
illustrate the sensitivity and fidelity of the scheme even with low signal-tonoise.
©2008 Optical Society of America
OCIS codes: (320.7100) Ultrafast Measurements; (140.7090) Ultrafast Lasers.
References and links
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C. Iaconis and I. A. Walmsley, “Self-referencing spectral interferometry for measuring ultrashort optical
pulses,” IEEE J. Quantum Electron. 35, 501-509 (1999).
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spectral phase characterization and compensation,” Opt. Lett. 29, 775-777 (2004).
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phase and intensity from correlation and spectrum only (PICASO),” J. Opt. Soc. Am. B 19, 330-339
(2002).
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measurement," Opt. Lett. 26, 932-934 (2001).
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(ICE) functions,” Apl. Phys. B 87, 655-663 (2007).
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Received 13 Jun 2008; revised 18 Jul 2008; accepted 18 Jul 2008; published 23 Jul 2008
4 August 2008 / Vol. 16, No. 16 / OPTICS EXPRESS 11782
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pulse characterization,” Opt. Lett. 32, 2822-2824 (2007).
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1. Introduction
Characterization of the amplitude and phase of ultrashort laser pulses is vital to the controlled
use of femtosecond laser systems [1, 2]. The diagnosis of such short pulses by means of
direct electronic detection is limited by instrument bandwidth. Optical sampling techniques
based on nonlinear autocorrelations or cross-correlations, as well as self-referencing spectral
interferometric methods remain the most viable means of means for characterizing such short
pulses. The first order or linear correlation contains spectral amplitude information, but
provides no information on the phase of the ultrashort pulse. Nonlinear schemes such as
second harmonic generation (SHG), two-photon fluorescence, two-photon conductivity and
Kerr gating provide intensity autocorrelations that are routinely used to estimate the laser
pulse width. No chirp (or phase) information is gained unless an interferometric setup such as
a second-order interferometric auto-correlation (IAC) is used [2]. Furthermore, while the IAC
contains amplitude and phase information, it will not yield a full characterization of the
amplitude and phase of the pulse electric field. A number of elegant techniques [3-7] have
been introduced that reconstruct the full electric field. The uniqueness of the retrieved
spectral phase (or its ambiguity) typically varies with the degree of complexity in the
implementation of these measurements. In many applications, however, the full field retrieval
may not be necessary, and only a semi-quantitative yet sensitive measure of the phase
distortion is of interest. The Modified Spectrum Auto-Interferometric Correlation (MOSAIC)
algorithm detailed here achieves this by a very simple approach: an IAC trace is converted to
a fringe free trace that provides a visual and unambiguous indication of the phase distortion
(chirp) with very high sensitivity [8, 9]. The algorithm runs efficiently on a PC to allow
precise experimental optimization in real-time.
The usefulness of MOSAIC has been extended with homodyne detection and signal
averaging [9]. High fidelity traces are extracted using fringe-free averaging techniques in a
high noise environment where the signal-to-noise ratio (SNR) approaches unity. Different
pulses can be distinguished even when producing essentially identical IAC traces [10]. The
fringe-free MOSAIC technique has recently been used to characterize ultrashort pulses in the
mid-IR [11], because MOSAIC is an algorithm, it can be performed in any spectral region or
experimental condition where an IAC or intensity autocorrelation and second harmonic
spectrum can be measured. The MOSAIC envelope is not distorted by intensity imbalance in
the autocorrelator, unlike an IAC. Additionally, MOSAIC traces are unaffected by residual
linear absorption that may be present in two photon absorbing detectors. Other pulse
characterization techniques depict temporal asymmetry using unbalanced interferometric
correlation envelope (ICE) functions [12].
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Although MOSAIC was originally intended for sensitive real-time analysis, we show here
that the MOSAIC trace can also be used to retrieve the spectral phase and provide a full field
reconstruction of an ultrashort pulse. In doing so, it is critical to eliminate potential
ambiguities. The speed and simplicity of this 1-dimensional algorithm comes at the expense
of time-direction ambiguity, but such precise knowledge is not necessary for many
applications. Unique quantitative analysis gives information on the electric field, amplitude
and phase in the presence of extreme noise; pulses with energy as low 60 pJ in 86 MHz pulse
trains with an average power of 5 mW have been analyzed using averaged MOSAIC
envelopes [9]. This is comparable to SHG FROG sensitivities [13]. A single autocorrelator is
all that is required to implement this technique. A sketch of the experimental layout is shown
in Fig. 1.
corner cube
E (t)
delay τ
E (t+τ)
chirped pulse
ultrafast
beam splitter
SHG crystal
Filter
linear
detector
Linear interferogram
nonlinear
detection
linear detector
LED
2 nd order
IAC
digital oscilloscope
Fig 1. Experimental setup for MOSAIC based phase retrieval. Two nonlinear detection
methods are shown; SHG followed by linear detection and two photon photoconductivity using
an LED.
This paper is organized as follows. The MOSAIC algorithm is presented followed by an
example of spectral reconstruction using an iterative technique. Ambiguities in the
reconstruction are discussed, demonstrated experimentally and finally a possible solution is
presented. Algorithm structure and efficiency is discussed as well as a comparison to IAC
based phase retrieval. MOSAIC reveals spectral phase distortion introduced by common twophoton photovoltaic detectors such as LEDs and planar metal-semiconductor-metal (MSM)
photoconductive devices. Finally, pulse characterization is performed in high noise conditions
where a stand-alone IAC is not possible.
2. Background
The simple principle of generating a MOSAIC trace can be described in the frequency domain
as follows: The laser pulse is assumed to have an electric field given
~
by E (t ) = f (t )e o
, where φ (t ) denotes the temporal phase. The well known second
order IAC trace produced by a nonlinear autocorrelator is given by [1]:
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i[ ω t +φ ( t )]
Received 13 Jun 2008; revised 18 Jul 2008; accepted 18 Jul 2008; published 23 Jul 2008
4 August 2008 / Vol. 16, No. 16 / OPTICS EXPRESS 11784
S IAC (τ ) = 1 + 2 ∫ f (t ) f (t + τ )dt
+ ∫ f (t ) f (t + τ ) cos(2ωτ + 2Δφ )dt
+ 2∫ f 1 / 2 (t ) f 3 / 2 (t + τ ) cos(ωτ + Δφ )dt
(1)
+ 2∫ f 3 / 2 (t ) f 1 / 2 (t + τ ) cos(ωτ + Δφ )dt
where Δφ (t ,τ ) = φ (t + τ ) − φ (t ) and
∫
f (t ) dt = 1 . The spectrum (Fourier transform) of the
above IAC contains three components at 0, ω and 2ω , where ω is the fringe frequency.
The spectrum is then modified by retaining the dc term, removing the ω term and
multiplying the 2ω component by 2 [14]. The inverse Fourier transform produces a fringeresolved MOSAIC trace [8]
S MOSAIC (τ ) = g (τ )+ | g~2ω (τ ) | cos[ 2ωτ + Φ (τ )]
(2)
where g (τ ) = ∫ f (t ) f (t + τ )dt is the intensity autocorrelation, and
g~2ω (τ ) = ∫ f (t ) f (t + τ )e −2iΔφ ( t ,τ ) dt
(3)
is the amplified 2ω component of the IAC, which is also the envelope of the second
harmonic field autocorrelation. The term, Φ (τ ) = − tan −1{Im[ g~2ω (τ )] / Re[ g~2ω (τ )]} , is the fringe
phase. The upper and lower envelopes of the MOSAIC trace are given by [15]
S MOSAIC (τ ) = g (τ )± | g~2ω (τ ) | .
(4)
Note that the lower envelope, S min (τ ) = g (τ ) − | g~2ω (τ ) | , exhibits a flat feature (i.e. equals
zero for all τ ) when no chirp is present ( Δφ = 0 ) and thus provides a sensitive and
background-free signal indicative of pulse chirp, see Fig. 2(a).
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1.0
(a)
1.0
1.0
(b)
1.0
0.8
0.8
0.8
0.8
0.6
0.6
0.4
0.4
0.2
0.6
0.2
0.6
0.0
0.0
-200
-100
0.4
0
100
200
-200
-100
0.4
Delay (fs)
0.2
0
100
200
Delay (fs)
0.2
0.0
0.0
-200
-100
1.0
0
100
200
-200
-100
1.0
Delay (fs)
0
100
200
100
200
Delay (fs)
(d)
(c)
0.8
0.8
0.6
0.6
0.4
0.4
0.2
0.2
0.0
0.0
-0.2
-0.2
-200
-100
0
100
200
-200
-100
0
Delay (fs)
Delay (fs)
Fig. 2. MOSAIC signals generated from the same pulse and detected using (a) a BBO crystal
and a linear detector (pulse duration: 60 fs FWHM) and (b) a two photon absorbing LED (76 fs
FWHM). Insets shows fringe resolved MOSAIC. Measured IAC signals from which the
MOSAIC traces in (a) and (b) were derived are shown in (c) and (d), respectively. The
structure of the IAC waveforms appears almost identical, while the MOSAIC traces reveal
chirp induced by the detection method.
Examples of two different chirp conditions in MOSAIC traces are shown in Fig. 2(a, b)
where the presence of shoulders on S min indicate chirp; the corresponding IAC traces appear
indistinguishable, Fig. 2(c, d). The upper MOSAIC envelope is a measure of pulse duration
but does not possess information about chirp information beyond S min . For comparison and
later discussion the fringe resolved MOSAIC is also depicted in Fig. 2(a, b) insets. We find it
more useful to replace the upper envelope by the intensity autocorrelation g (τ ) so that
S max (τ ) = g (τ ) and
Smin (τ ) = g (τ )− | g~2ω (τ ) | represent amplitude and phase profiles
respectively [9].
The ideal condition S min (0) = 0 can only be approached in practice. Small deviations in
the order of quadratic nonlinearity, minor misalignment of the interferometric autocorrelator
or insufficient bandwidth in the IAC acquisition may lead to a distorted trace for which
S min (0) is nonzero. This can be addressed with a correction factor η :
S min (τ ) = g (τ ) − η | g~2ω (τ ) |
(5)
that forces the trace to zero at zero delay. This correction takes the form:
η=
g (0) − S min (0)
| g~2ω (0) |
(6)
and is needed to render a correct MOSAIC, separating pulse chirp information from distortion
associated with autocorrelator misalignment and noise in the detection electronics. The
second harmonic field autocorrelation is related to the second harmonic spectrum by
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4 August 2008 / Vol. 16, No. 16 / OPTICS EXPRESS 11786
~
g~2ω (τ ) = F −1{| E (2ω ) |2 }
(7)
where the second harmonic power spectrum is | E (2ω ) |2 and F −1 denotes the inverse Fourier
transform operation [15]. This property can be exploited to acquire MOSAIC traces in a noninterferometric setting suitable for single shot measurements and is termed EnvelopeMOSAIC (or E-MOSAIC). The production of MOSAIC envelopes from the intensity
autocorrelation and the second harmonic spectrum has been shown to provide the same
information as first-generation MOSAIC generated from a fringe resolved IAC [15]. An
additional rendering of MOSAIC in the form of a Hybrid-MOSAIC (H-MOSAIC) has been
developed that distinguish between temporal and spectral phase [15]. The added capability of
the H-MOSAIC requires the pulse spectrum in addition to the IAC. In rapid-scan IAC
schemes, the spectrum can be obtained by splitting off a small amount of the autocorrelator
output and directing it to a linear detector [9, 15 and 16], see Fig. 1. The pulse spectrum,
~
| E (ω ) |2 , is found from the Fourier transform of the resulting linear interferogram (or a
spectrometer in a noninterferometric or single shot arrangement). The spectrum can be used
to compute the complex transform limited amplified 2ω component of the IAC by
employing the convolution integral:
~
~
g~2TLω (τ ) = F −1{∫ | E (ω ' ) || E (ω − ω ' ) | dω '} .
(8)
~ (τ ) is not important for visual appreciation of pulse distortion at this stage,
The use of g
2ω
but may play a role for phase retrieval as will be discussed in the next section.
TL
2. Developments
2.1 Retrieval MOSAIC: spectral phase reconstruction
Naganuma et. al. have shown that a combination of pulse spectrum and IAC is sufficient to
uniquely reconstruct the complex electric field with only a time direction ambiguity [17].
Reconstruction results from SHG FROG or GRENOUILLE devices also have the direction of
time ambiguity [3, 7]. An iterative, phase retrieval technique using IAC and pulse spectrum
based on a population split genetic algorithm has recently shown promise for improved
computational efficiency and accuracy [18]. The SNR required to uniquely reconstruct the
phase, however, may not be experimentally practical [10]. By combining MOSAIC data with
the first-order interferogram and performing additional analysis, the spectral phase of the
electric field can be recovered [14].
Experimental reconstruction using MOSAIC and the pulse spectrum has been
demonstrated using an iterative line minimization technique [16]. In this reconstruction
method, all points in the spectral phase are optimized individually at the expense of
processing time. It has been shown that processing time can be reduced about 7x by
analyzing phase with a fourth-order Taylor-series expansion and adjusting the coefficients
using the Retrieval (R)-MOSAIC algorithm [15]. The sequential R-MOSAIC algorithm
accounts for the predominant contribution of the lowest order spectral phase coefficients
encountered in realistic pulses. Our simulations have shown that simultaneous optimization
of Taylor-series coefficients is less likely to converge on the optimal phase. Simultaneous
optimization can weight higher order terms too heavily causing the algorithm to fall into a
local well far from the correct solution in the search space. This was noted as a poorly
reconstructed MOSAIC with a higher root mean square (RMS) error compared to the
sequential routine.
The sequential R-MOSAIC algorithm minimizes the RMS error, Δ , between the measured
and reconstructed MOSAIC traces. The function to be minimized is given by:
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1/ 2
N
⎧ 1 ⎡N
⎤⎫
2
Δ=⎨
(
S
−
s
)
+
( S min,k − s min,k ) 2 ⎥ ⎬
∑
∑
max, k
max, k
⎢
k =1
⎦⎭
⎩ 2 N ⎣ k =1
(9)
where N is the number of points used in the reconstruction, S max/ min and smax/ min represent
measured and computed quantities, respectively. Minimization of the RMS defines
convergence of the algorithm. An example of a fit ( Δ = 0.0068 ) is shown in Fig. 3(b).
IAC
1
MOSAIC
1
(a)
0.8
measured
Signal (a. u.)
0.8
Signal (a. u.)
reconstructed
(b)
0.6
0.4
0.6
Δ = 0.0068
100 x average
0.4
0.2
0.2
0
0
-400
-200
0
Delay (fs)
200
400
-400
Pulse Frequency Domain
1
0
-1
Intensity (a. u.)
2
Phase (rad)
Signal (a. u.)
400
20
(d)
3
0.5
200
1
4
(c)
0
Delay (fs)
Pulse Time Domain
5
1
-200
10
0.8
0.6
0
0.4
-10
0.2
-20
Phase (rad)
-0.2
-2
-3
-4
0
-200
-100
0
100
Angular Frequency (Hz) x 1e12
-5
200
0
-400
-200
0
Time (fs)
200
400
-30
Fig. 3. (a) Measured second-order IAC (b) Experimental MOSAIC (pink lines) and
reconstructed MOSAIC (dots) from the measured spectrum and retrieved phase of (c). Time
domain pulse (d).
For pulses not having a Taylor-series expandable phase it becomes necessary to use an
individual point line search method to adequately reconstruct MOSAIC traces. Such
algorithms can be seeded with the output of the resulting spectral phase from R-MOSAIC to
allow for more rapid convergence. An experimental example of a phase recovered from
individual point line search is shown across a measured spectrum in Fig. 3(c). Simultaneous
recording of both linear and second-order interferometric autocorrelation traces is done on a
two-channel digital oscilloscope controlled with National Instruments LabVIEW software.
The associated IAC and 100x averaged MOSAIC trace can be seen in Fig. 3(a) and (b) with
the retrieved time domain pulse displayed in Fig. 3(d). While a low error was achieved in the
pulse retrieval, we show in the next section that ambiguities in the phase retrieval error map
require additional information to be used to correctly identify the spectral phase.
2.2 Error mapping
Spectral phase retrieval using the three envelope dataset outlined by Naganuma et. al. can be
improved with the application of the MOSAIC algorithm. Here we show that preprocessing
of the IAC to a MOSAIC trace leads to better localization of the error minima in the
parameter space explored by iterative retrieval algorithms. To demonstrate this we simulate
an ultrashort pulse having a symmetric spectrum given by E~(ω ) = exp[−ω 2 /(Δω ) 2 ] . The
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spectrum is centered at 800 nm and has a FWHM ≈ 57 nm. A spectral phase is assigned to the
pulse having 50 fs2 of GVD and 175 fs3 of TOD. From this pulse a target IAC, Eq. (1), and a
fringe resolved MOSAIC, Eq. (2), are computed. To visualize ambiguities in the retrieval we
use the trial pulses to produce an error map in a manner similar to Ref. [6]. An error map as a
function of GVD and TOD is produced by computing the RMS error between the target IAC
and the trial IAC traces, Fig. 4(a).
80
0
0.002
0.004
0.006
0.008
0.010
0.012
0.014
0.016
0.018
0.020
(a)
60
40
GVD (fs2)
Fringe-MOSAIC
Error
20
0
-20
-40
-60
-80
-100
100
80
40
20
0
-20
-40
-60
-80
-100
80
0
0.002
0.004
0.006
0.008
0.010
0.012
0.014
0.016
0.018
0.020
(c)
60
GVD (fs2)
40
20
0
-20
-40
-60
-80
-100
-600 -400 -200 0 200 400 600
TOD (fs3)
E-MOSAIC + DFP
Error
100
80
(d)
60
40
GVD (fs2)
Envelope-MOSAIC
Error
0
0.002
0.004
0.006
0.008
0.010
0.012
0.014
0.016
0.018
0.020
-600 -400 -200 0 200 400 600
TOD (fs3)
-600 -400 -200 0 200 400 600
TOD (fs3)
100
(b)
60
GVD (fs2)
Interferometric correlation
100
20
0
-20
-40
-60
-80
-100
Error
0
0.002
0.004
0.006
0.008
0.010
0.012
0.014
0.016
0.018
0.020
-600 -400 -200 0 200 400 600
TOD (fs3)
Fig. 4. Error maps for (a) an IAC, (b) a fringe resolved MOSAIC, (c) E-MOSAIC and (d) EMOSAIC with DFP on a pulse having GVD, TOD and a symmetric spectrum.
Trial reconstruction pulses are produced by taking the target spectral amplitude and
assigning a trial spectral phase for all values of GVD between -100 and 100 fs2 and TOD
between -600 and 600 fs3. Regions of dark blue indicate low error. The corresponding error
map for the target fringe resolved MOSAIC and trial fringe resolved MOSAIC trace is shown
in Fig. 4(b). It is important to note that the region of low error surrounding the solution
shrinks in the case of the fringe resolved MOSAIC. This highly localized solution suggests
faster convergence and better accuracy for iterative phase retrieval schemes. In the case of an
intensity imbalanced autocorrelator or residual linear absorption on the detector the IAC error
map would be affected. Because the ω term is removed and it is background free, MOSAIC
error maps are insensitive to these distortions.
We further compute the error map for the case of the E-MOSAIC, Eq. (4). Results are
presented in Fig. 4(c). Here it can be seen that regions of low error become broader and less
localized relative to the fringe resolved case. In addition, there is also four-fold degeneracy
for the region of lowest error. This degeneracy results in ambiguity on the sign of GVD and
TOD coefficients. The ambiguity and poor localization is due to neglecting the fringe phase,
Φ(τ ) , from Eq. (2). For visual interpretation of the E-MOSAIC trace, Φ(τ ) is not needed,
however, for phase retrieval its inclusion is important particularly when the pulse spectrum is
symmetric. Figure 4(c) suggests that E-MOSAIC depends weakly on TOD. Incorporation of
the fringe phase is needed to better define the solution in the error map. Experimentally,
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Φ(τ ) can be signal averaged in the same way as a MOSAIC envelope. Spectral dependence
~TL (τ ) presented in Eq.
on the fringe phase can be removed by making use of the phase on g
2ω
(8) in section 1. The spectral dependence present on Φ (τ ) is also present on Φ (τ ) since
they are both produced from the same spectrum and the same rapid scan autocorrelator. By
subtracting the two we can have a background free measure of a differential fringe phase
(DFP)
TL
δ DFP (τ ) = Φ(τ ) − ΦTL (τ ) .
(10)
The DFP is no longer coupled with spectral dependence if a single interferometer is used.
With a spectrometer (as in a single shot) it is important to have a well calibrated frequency
axis as uncertainties in determining ω and 2ω can affect retrieval. The DFP is only
sensitive to spectral phase. The value of the DFP can become large far from zero delay
where g (τ ) goes to zero. To accentuate the relevant features of the DFP we weight it by the
amplitude of g (τ ) . The normalized DFP is then g (τ )[Φ (τ ) − Φ (τ )] . Simulation for
different dispersion conditions is shown in Fig. 5.
TL
Fig. 5. Normalized DFP signals showing sensitivity to relative sign on GVD and TOD
dispersion coefficients.
It is important to note that the DFP is sensitive to the relative sign of GVD and TOD
coefficients across a symmetric spectrum while E-MOSAIC is not. If both GVD and TOD
have the same sign, the DFP will show a peak followed by a valley, Fig. 5(blue line), while an
opposite sign between the coefficients is seen as a valley followed by a peak, Fig. 5(red line).
The inclusion of the DFP to E-MOSAIC is shown in the error map of Fig. 4(d). The error
function in this case includes equal weighting for the upper and lower envelopes of MOSAIC
as well as the normalized DFP. Spurious solutions are eliminated and highly localized
solutions are restored, indicating the DFP is an important contribution to E-MOSAIC based
phase retrieval for pulses having a symmetric spectrum.
To demonstrate the experimental relevance of the DFP we consider the retrieved pulse
from Fig. 3. The highly symmetric spectrum allows for the ambiguity displayed in Fig. 4(c).
Identically reconstructed MOSAIC traces were produced with different retrieved pulses by
changing the sign of the starting point in the iterative line search algorithm. The retrieved
pulses from the different starting points are shown in Fig. 6(a, b). The red line is a polynomial
fit across the FWHM of the pulse spectrum to the retrieved spectral phase. The fit in Fig. 6(a)
φ1 (ω ) = −466 fs 2ω 2 − 11700 fs3ω 3 while the fit in Fig. 6(b) is
φ2 (ω ) = −1000 fs 2ω 2 + 11900 fs 3ω 3 . The sign difference between the GVD and TOD
is
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Received 13 Jun 2008; revised 18 Jul 2008; accepted 18 Jul 2008; published 23 Jul 2008
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coefficients in the two fits is a nontrivial ambiguity that can be resolved with the DFP. The
DFP for each reconstructed pulse is shown in the insets. The correlation of the peaks and
valleys of the reconstructed DFP with the measured DFP indicates correct sign for the pulse in
Fig. 6(a). Similarly, the anti-correlation of the peaks and valleys of the DFP shown in Fig.
6(b) indicate that it is not the correct pulse; despite the fact that its reconstructed E-MOSAIC
is the same.
Note that the advantage of the E-MOSAIC trace is that it can be averaged for pulse
reconstruction at very low pulse energies. If sufficient pulse energy is available, however, the
fringe-resolved MOSAIC trace can be used for full pulse reconstruction and issues of
ambiguities in the E-MOSAIC error map can be avoided. The DFP can be included with EMOSAIC in the reconstruction algorithm. This will automatically resolve sign ambiguities.
Because the DFP can become distorted from asymmetries in the IAC and noise, the
autocorrelator must be aligned to obtain a symmetric IAC and averaging is needed to suppress
noise.
Pulse 2 Frequency Domain
Pulse 1 Frequency Domain
5
5
Recon.
Recon.
(a)
(b)
4
Peak/Valley Correlation
0.6
3
0
-1
Meas.
Peak/Valley anti-correlation
2
1
0.4
0.8
Signal (a. u.)
Meas.
Phase (rad)
0.8
Signal (a. u.)
1
0.6
-3
-100
0
100
Angular Frequency (Hz) x 1e12
-5
200
2
0
0.4
-1
-2
0.2
-3
-4
0
-200
3
1
-2
0.2
4
Phase (rad)
1
-4
0
-200
-100
0
100
Angular Frequency (Hz) x 1e12
-5
200
Fig. 6. (a) A retrieved pulse from E-MOSAIC and DFP showing peak/valley correlation (inset).
(b) A second pulse reconstructing the same E-MOSAIC, however, the DFP (inset) shows
peak/valley anti-correlation indicating it is not the correct pulse. Polynomial fit to the phase
across the FWHM of pulse spectrum is shown in red.
Several optimization algorithms have been investigated for effectiveness in phase retrieval
using the procedure outlined above. Performance is defined by the minimum achievable RMS
error and processing time. We tested line search, simplex, Levenberg-Marquardt, genetic and
pattern search algorithms. Each of these algorithms was unmodified from the standard code
available in the MATLAB software platform. Analysis of mode-locked Ti:sapphire laser
pulses are presented in Fig. 7.
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4 August 2008 / Vol. 16, No. 16 / OPTICS EXPRESS 11791
Relative Algorithm Performance
1
Computation Time
Error
0.75
0.5
0.25
0
Quasi-Newton
line search
Nelder-Meade
Simplex
LevenbergMarquardt
Genetic
Algorithm
Pattern Search
Fig. 7. Normalized algorithm performance for spectral phase retrieval on our 60 fs Ti:sapphire
laser pulses. All algorithms are evaluated with the MATLAB software platform.
Best phase reconstruction is obtained with either a simplex or line search routine. The genetic
and pattern search algorithms also produce satisfactory fits, but do so at the expense of
computation time.
3. Additional examples
3.1 Two-photon conductivity induced dispersion
By applying the MOSAIC algorithm to IAC traces generated in different ways but originating
from the same pulse we are able to perform detector characterization. We generate IAC traces
of near-infrared mode-locked Ti:sapphire laser pulses by (i) frequency doubling with a BBO
crystal into a linear detector, Fig. 2(c) and (ii) two-photon absorption-induced photocurrent in
a light-emitting diode [19], Fig. 2(d). The MOSAIC algorithm is applied to both IAC
waveforms to generate the envelopes shown in Figs. 2(a) and 2(b), respectively. While the
IACs appear essentially the same, the MOSAIC waveforms reveal a striking difference.
The pronounced shoulders in Fig. 2(b) indicate the LED detection scheme introduces chirp
that is nearly five times greater than the same pulse detected via second-harmonic generation
with BBO. We measure LEDs from four different manufacturers and find in all cases higher
chirp and longer pulse duration compared to second-order autocorrelation with a BBO crystal.
The chirp introduced by the LED epoxy dome is negligible for our 60 fs pulses. Removal of
the dome is desirable, however, for enhanced SNR and ease of alignment. MOSAIC also
reveals significant variation in induced chirp for identical wavelength LEDs produced by the
same manufacturer. A detailed comparison of LED and BBO detection can be found in Ref.
[20].
The metal-semiconductor-metal (MSM) structure can be used as a two-photon detector in
second order autocorrelations; we examine a ZnSe MSM device for induced pulse chirp [21].
The structure is a single crystal ZnSe substrate with interleaved titanium electrodes and a gold
cap layer. Titanium provides high adhesion to the ZnSe [22]. A 3.5x microscope objective
focuses the autocorrelator output on the region between the electrodes (bias: 30 V). A 500
average MOSAIC is obtained for different lens positions; shoulder height (chirp) is plotted in
Fig. 8(a). Error bars are due to slight asymmetry in MOSAIC traces. The SNR of the second
order IAC is shown in Fig. 8(b). Lowest chirp occurs with the focusing geometry that
produces the highest SNR, but this alignment optimization must be made with caution.
Changing the lens position shifts the region of two-photon absorption from the surface to
deeper in the bulk ZnSe. Significant chirp is introduced as the interaction length in the
material increases, while the SNR decreases by < 10%.
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18
(b)
24
22
14
20
12
Signal-to-Noise
MOSAIC Shoulder Height (%)
16
26
(a)
10
8
6
18
16
14
12
10
4
8
2
6
0
0
100
200
300
400
Path Length (micron)
0
500
100
200
300
400
500
Path Length (micron)
Fig. 8. (a) Chirp response in MOSAIC and (b) signal-to-noise of the second order IAC as an
MSM detector is brought through focus.
3.2 Characterization using MOSAIC in low signal-to-noise
MOSAIC exhibits high fidelity even when SNR is poor. Figure 9(a) shows an autocorrelation
of a frequency doubled Ti:sapphire at λ = 415 nm obtained with an ultraviolet Michelson
interferometer and suitably cut BBO crystal to produce 208 nm light that is detected with a
photo-multiplier tube. An averaged MOSAIC waveform generated from 1000 low SNR IAC
traces is presented in Fig. 9(b). Pulse duration and chirp can be determined with excellent
accuracy.
0.8
(a)
1.0
Signal (a. u.)
0.6
(b)
0.8
0.4
0.6
0.2
0.4
0.0
0.2
-0.2
0.0
-0.4
-300 -200 -100
0
100
Delay (fs)
200
300
-300 -200 -100
0
100
200
300
Delay (fs)
Fig. 9. (a) Single IAC trace just above the noise level of a frequency doubled mode-locked
Ti:sapphire laser pulse (
1000 noisy IAC traces.
λ
= 415 nm). (b) Averaged MOSAIC waveform produced from
4. Summary
We have presented the principle of the MOSAIC algorithm for ultrashort pulse
characterization. Ambiguities in MOSAIC based phase retrieval were presented and
experimentally demonstrated. A solution for avoiding ambiguities was developed. Error
maps showing high localization of phase retrieval were presented.
Second-order
autocorrelations can introduce significantly different amounts of chirp depending on the
implementation. Second harmonic generation and linear detection produces less distortion
compared to two-photon absorbing LEDs. The metal-semiconductor-metal structure
introduces minimal chirp provided the light is focused near the surface to achieve maximum
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Received 13 Jun 2008; revised 18 Jul 2008; accepted 18 Jul 2008; published 23 Jul 2008
4 August 2008 / Vol. 16, No. 16 / OPTICS EXPRESS 11793
SNR. MOSAIC is helpful for characterizing very weak ultrashort pulses such as from
frequency doubled Ti:sapphire lasers. The efficiency of different optimization algorithms was
also discussed. Open source MOSAIC software is available for free download at
http://www.optics.unm.edu/sbahae/.
Acknowledgment
The authors wish to acknowledge helpful discussions with B. Yellampalle and M. Hasselbeck.
Support provided through National Science Foundation awards ECS-0100636 and DGE0114319 is gratefully acknowledged.
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