SlideShare ist ein Scribd-Unternehmen logo
1 von 41
Introduction optical pulse
measurement & Fiber clear
Wei-Yi Tsai
Institute of Photonics Technologies
National Tsing Hua University, Taiwan
Feb,14, 2011
NTHU
Outline
 Defined of ultrafast
 Mathematic introduce
 definition
 Correlation & Convolution
 Pulse measurement methods
 Field autocorrelation
 Cross correlation
 Intensity autocorrelation
 Homework 2
NTHU
Defined of ultrafast
 What is ultrafast ?
 The range of ultrafast ?
‘’ ultrashort’’ refers to the femtosecond(fs) to picosecond(ps)
range.
3
Milli- Micro- Nano- Pico- Femto- Atto-
Time(s) 10e-3 10e-6 10e-9 10e-12 10e-15 10e-18
frequency 1kHz 1MHz 1GHz 1THz 1PHz 1EHz
NTHU
Goal of pulse measurement
4
* ( )1
( ) Re{ ( ) } { ( ) ( ) }, ( ) ( )
2
o o oj t j t j t j t
E t a t e a t e a t e a t a t e   
      
-10 -8 -6 -4 -2 0 2 4 6 8 10
-1
-0.8
-0.6
-0.4
-0.2
0
0.2
0.4
0.6
0.8
1
X: 0.5811
Y: 0.6262
( )t
( )a t
It is straightforward to get carrier frequency by spectrometer,
we focus on measuring the complex envelope function
NTHU
Difficult
 The laser pulse duration cannot be easily measured by
optoelectronic methods, since the response time of
phtodetector and oscilloscopes are at best of the order of
200(fs)
5(Shang-Da Yang, Ultrafast Optics, Lecture slide 05)
NTHU
Definition
 For a given power spectrum , the pulse is :
 Transform-limited (TL), if
 Chirped, if is nonlinear
6
2
( )A 
( ) 0  
( ) 
-10 -8 -6 -4 -2 0 2 4 6 8 10
-1
-0.8
-0.6
-0.4
-0.2
0
0.2
0.4
0.6
0.8
1
-10 -8 -6 -4 -2 0 2 4 6 8 10
-1
-0.8
-0.6
-0.4
-0.2
0
0.2
0.4
0.6
0.8
1
Chirped TL
NTHU
Pulse measurement method
 Because the pulses are so short that no existing electronics
are capable of resolving them, so the common approach is
to measure the ultrashort pulse by itself
 Auto-correlation
 Cross-correlation
7
*
12 1 2( ) ( )f a t a t dt


 
*
( ) ( )f a t a t dt


 
NTHU
Outline
 Defined of ultrafast
 Mathematic introduce
 Correlation & Convolution
 Pulse measurement methods
 Field autocorrelation
 Cross correlation
 Intensity autocorrelation
 FAQ
8
NTHU
Field autocorrelation
9(Shang-Da Yang, Ultrafast Optics, Lecture slide 05)
NTHU
Field autocorrelation trace formula
10
0
1( ) ( ) 1 Re{ ( ) }j
FA outI P G e  
    
11 01 ( ) cos( ( ))GG R       
when
1
*
( )
1 12
( ) ( )
( ) ( )
( )
Gja t a t
G G e C
a t
 
 

  
Is the normalized field autocorrelation function of ( )a t
NTHU
Example A TL pulse with two smallside lobes
11(Shang-Da Yang, Ultrafast Optics, Lecture slide 05)
NTHU
How to retrieve G1 from the field autocorrelation
 Perform Fourier transform for trace:
 Extract the component centered at :
 Shift to the baseband:
 Perform inverse Fourier transform:
12
{ ( )}FA FAI F I 
, 0( ) ( )oFA FAI I     
0,0 , 0( ) ( )FA FAI I       
1
,01( ) { ( )}FAG F I 
 
11 0( ) 1 ( ) cos( ( ))FA GI G       
(Shang-Da Yang, Ultrafast Optics, Lecture slide 05)
NTHU
Limitation
 FA function is nothing but power spectrum of the field
envelope a(t):
 As a result
 NO spectral phase information , then we cannot
distinguish transform-limited pulse with
long chirped pulse with and even
incoherent noise
13
2
1{ ( )} ( )F G A 
( ) 
( )TLI t ( ) 0  
( )chirpI t
2
2
( )
2
 
  
( )noiseI t
NTHU
Limitation
14
-5 -4 -3 -2 -1 0 1 2 3 4 5
0
0.5
1
1.5
2
2.5
3
Temporal intensity profile
Time t
Intensity(a.u)
-5 -4 -3 -2 -1 0 1 2 3 4 5
0
0.2
0.4
0.6
0.8
1
1.2
1.4
1.6
1.8
2
Field autocorrelation trace

-5 -4 -3 -2 -1 0 1 2 3 4 5
0
1
2
3
4
5
6
7
Temporal intensity profile
Time t
Intensity(a.u)
-5 -4 -3 -2 -1 0 1 2 3 4 5
0
0.2
0.4
0.6
0.8
1
1.2
1.4
1.6
1.8
2
Field autocorrelation trace

NTHU
Limitation
15
-5 -4 -3 -2 -1 0 1 2 3 4 5
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
Temporal intensity profile
Time t
Intensity(a.u)
-5 -4 -3 -2 -1 0 1 2 3 4 5
0
0.2
0.4
0.6
0.8
1
1.2
1.4
1.6
1.8
2
Field autocorrelation trace

11 0( ) 1 ( ) cos( ( ))FA GI G R        
NTHU
limitations
 NO pulse asymmetry information, for
16
( ) ( )FA FAI I  
(Shang-Da Yang, Ultrafast Optics, Lecture slide 05)
NTHU
Outline
 Defined of ultrafast
 Mathematic introduce
 Correlation & Convolution
 Pulse measurement methods
 Field autocorrelation
 Cross correlation
 Intensity autocorrelation
 FAQ
17
NTHU
Field-Cross-correlation
18
(Shang-Da Yang, Ultrafast Optics, Lecture slide 05)
NTHU
Field-cross-correlation

The field cross-correlation function of and
19
0
1,( ) ( ) 2Re{ ( ) }j
Fx out s r xI P U U G e  
     
1, 0 1,2 ( ) cos( ( ))tot x G xU G       
*
1, ( ) ( ) ( )x s rG a t a t C   
( )sa t ( )ra t
2
( )i iT
U a t dt 
NTHU
Field cross-correlation
 For very short reference pulse
20
r st t 
0 1,2 ( ) cos( ( ))tot s G xU a       
1, 0 1,( ) 2 ( ) cos( ( ))FX tot x G xI U G       
1, ( ) ( ) ( ) ( )x s sG a t t a     
NTHU
Field cross-correlation
 Perform Fourier transform for the trace
 Extract the component centered at
 Shift to the baseband
21
0
1,( ) 2Re ( ) j
FX tot xI U G e  
  
*
1, ( ) ( ) ( )x s rG a t a t  
{ ( )} ( )FXFXF I I   
* *
0 0 0 0( ) [ ( ) ( ) ( ) ( )]s r s rA A A A               
0
0, ( )FX oI   
0
*
,0 , 0( ) ( ) ( ) ( )FX FX s rI I A A         
NTHU
Field cross-correlation
 The exact complex spectrum of the signal pulse can be
derived by:
 If the complex spectrum of the reference pulse is
known
 Bandwidth of the reference pulse is broader than that of the
signal pulse
22
,0
*
( )
( )
Fx
s
r
I
A
A




( )rA 
NTHU
Poor signal-to-background contrast
23
Cross-correlation Field-autocorrelation
0
1,( ) ( ) 2Re{ ( ) }j
Fx out s r xI P U U G e  
     
(Assume: TL Gaussian, ),s r s rU U t t  
-10 -8 -6 -4 -2 0 2 4 6 8 10
0.7
0.8
0.9
1
1.1
1.2
1.3
1.4

cross-correlation
-10 -8 -6 -4 -2 0 2 4 6 8 10
0
0.2
0.4
0.6
0.8
1
1.2
1.4
1.6
1.8
2

Field-autocorrelation
NTHU
Outline
 Defined of ultrafast
 Mathematic introduce
 Correlation & Convolution
 Pulse measurement methods
 Field autocorrelation
 Cross correlation
 Intensity autocorrelation
 FAQ
24
NTHU
Second harmonic generation (SHG)
25
NLO
material
0 02
-1 -0.8 -0.6 -0.4 -0.2 0 0.2 0.4 0.6 0.8 1
x 10
-9
-1
-0.8
-0.6
-0.4
-0.2
0
0.2
0.4
0.6
0.8
1
-1 -0.8 -0.6 -0.4 -0.2 0 0.2 0.4 0.6 0.8 1
x 10
-9
-1
-0.8
-0.6
-0.4
-0.2
0
0.2
0.4
0.6
0.8
1
2 2
( )j t j t
e e 

2
2 ( )a a t 
NTHU
Intensity autocorrelation (IA)
26
NTHU
Fringe-resolved intensity autocorrelation

 …Intensity autocorrelation
 ……Intensity-field correlation
 ……Squared-field autocorrelation
27
2 0 0( ) 1 2 ( ) 4 ( ) cos( ( )) ( ) cos(2 ( ))FRIA f gI G f g                
2 2
( ) ( )
( )
( )
I t I t
G R
I t



 
*
2
[ ( ) ( )] ( ) ( )
( )
2 ( )
I t I t a t a t
f C
I t
 

  
 
* 2
2
[ ( ) ( )]
( )
( )
a t a t
g C
I t



 
NTHU
Comparison between TL &chirped pulses of the same I(t)
28(Shang-Da Yang, Ultrafast Optics, Lecture slide 05)
NTHU
How to retrieve G2 from the Intensity autocorrelation
 Perform Fourier transform for trace:
 Extract the component centered at :
 Remove the Dirac-function component
 Perform inverse Fourier transform:
29
{ ( )}FAIA FAIAI F I 
,0 ( ) ( 0)FRIA FRIAI I   
1
,02 ( ) { ( )}FRIAG F I 
 
(Shang-Da Yang, Ultrafast Optics, Lecture slide 05)
NTHU
Intensity autocorrelation trace
30
-1 -0.8 -0.6 -0.4 -0.2 0 0.2 0.4 0.6 0.8 1
x 10
-13
0
1
2
3
4
5
6
7
8
Ifria

-2 -1.5 -1 -0.5 0 0.5 1 1.5 2
x 10
16
-0.5
0
0.5
1
1.5
2
2.5
3
x 10
-13
2 ( )G 
-2 -1.5 -1 -0.5 0 0.5 1 1.5 2
x 10
16
-0.5
0
0.5
1
1.5
2
2.5
3
x 10
-13
X: 0
Y: 2.755e-013
NTHU
Intensity autocorrelation trace
31
-1 -0.8 -0.6 -0.4 -0.2 0 0.2 0.4 0.6 0.8 1
x 10
-13
0
1
2
3
4
5
6
7
8
Intensity autocorrelation trace

-1 -0.8 -0.6 -0.4 -0.2 0 0.2 0.4 0.6 0.8 1
x 10
-13
0
1
2
3
4
5
6
7
8
Intensity autocorrelation trace

2 ( )G 
NTHU
Deconvolution factor
I(t)
1.41 1.54 1
32
decR
2
2( / )pt t
e
 2
sec ( / )ph t t ( / )pt t
If this factor is know, or assumed, the time duration (Intensity width)
of a pulse can be measured using an Intensity autocorrelation
The deconvolution factor, defined as:
/decR t  
NTHU
limitation
 , no pulse asymmetry information.
33
2 2( ) ( )G G  
(Shang-Da Yang, Ultrafast Optics, Lecture slide 05)
NTHU
Noncollinear
34
0(2)
2 ( , ) ( ) ( ) j
a t a t a t e  
   
 
2
(3)
2 2( ) ( , ) ( ) ( ) ( )IAI a t dt I t I t G      
(Shang-Da Yang, Ultrafast Optics, Lecture slide 05)
NTHU
Homework
 試著利用 一個Gaussian pulse 做field -auto-correlation,
並畫出 .
35
FAI
NTHU
Fiber 材質種類
36
Single mode fiber
Polarization maintain fiber
Dispersion compensate/increasing fiber
NTHU
Fiber adaptor
37
PC-PC APC-APC
NTHU
Fiber 分類 & Different fiber patch cord connectors.
38PC APC
NTHU
Fiber clear
 Step1 先用棉花棒沾酒精
 Step2 再用棉花棒沿著fiber頭的面積擦一次
 Step3 最後用氮氣槍,把酒精吹乾
39
NTHU
Fiber clear
40
PC
APC
NTHU
End
41
Thanks for your listening

Weitere ähnliche Inhalte

Was ist angesagt?

Green Telecom & IT Workshop: Marceau greentouch
Green Telecom & IT Workshop: Marceau greentouchGreen Telecom & IT Workshop: Marceau greentouch
Green Telecom & IT Workshop: Marceau greentouchBellLabs
 
4 stochastic processes
4 stochastic processes4 stochastic processes
4 stochastic processesSolo Hermelin
 
Power Quality Monitoring by Disturbance Detection using Hilbert Phase Shifting
Power Quality Monitoring by Disturbance Detection using Hilbert Phase ShiftingPower Quality Monitoring by Disturbance Detection using Hilbert Phase Shifting
Power Quality Monitoring by Disturbance Detection using Hilbert Phase Shiftingidescitation
 
Introduction of GPS BPSK-R and BOC
Introduction of GPS BPSK-R and BOCIntroduction of GPS BPSK-R and BOC
Introduction of GPS BPSK-R and BOCPei-Che Chang
 
Ppt of analog communication
Ppt of analog communicationPpt of analog communication
Ppt of analog communicationArun Kumar
 
DSP_FOEHU - Lec 03 - Sampling of Continuous Time Signals
DSP_FOEHU - Lec 03 - Sampling of Continuous Time SignalsDSP_FOEHU - Lec 03 - Sampling of Continuous Time Signals
DSP_FOEHU - Lec 03 - Sampling of Continuous Time SignalsAmr E. Mohamed
 
Circuit Network Analysis - [Chapter2] Sinusoidal Steady-state Analysis
Circuit Network Analysis - [Chapter2] Sinusoidal Steady-state AnalysisCircuit Network Analysis - [Chapter2] Sinusoidal Steady-state Analysis
Circuit Network Analysis - [Chapter2] Sinusoidal Steady-state AnalysisSimen Li
 
Design and Implementation of Low Ripple Low Power Digital Phase-Locked Loop
Design and Implementation of Low Ripple Low Power Digital Phase-Locked LoopDesign and Implementation of Low Ripple Low Power Digital Phase-Locked Loop
Design and Implementation of Low Ripple Low Power Digital Phase-Locked LoopCSCJournals
 
Matlab implementation of fast fourier transform
Matlab implementation of  fast fourier transformMatlab implementation of  fast fourier transform
Matlab implementation of fast fourier transformRakesh kumar jha
 
Autoregression
AutoregressionAutoregression
Autoregressionjchristo06
 
Toward an Improved Computational Strategy for Vibration-Proof Structures Equi...
Toward an Improved Computational Strategy for Vibration-Proof Structures Equi...Toward an Improved Computational Strategy for Vibration-Proof Structures Equi...
Toward an Improved Computational Strategy for Vibration-Proof Structures Equi...Alessandro Palmeri
 
Filter-Type Fault Detection and Exclusion (FDE) on Multi-Frequency GNSS Receiver
Filter-Type Fault Detection and Exclusion (FDE) on Multi-Frequency GNSS ReceiverFilter-Type Fault Detection and Exclusion (FDE) on Multi-Frequency GNSS Receiver
Filter-Type Fault Detection and Exclusion (FDE) on Multi-Frequency GNSS ReceiverYi-Hsueh Tsai
 
Introduction to Communication Systems 2
Introduction to Communication Systems 2Introduction to Communication Systems 2
Introduction to Communication Systems 2slmnsvn
 
IVR - Chapter 3 - Basics of filtering II: Spectral filters
IVR - Chapter 3 - Basics of filtering II: Spectral filtersIVR - Chapter 3 - Basics of filtering II: Spectral filters
IVR - Chapter 3 - Basics of filtering II: Spectral filtersCharles Deledalle
 
Advances in the Solution of NS Eqs. in GPGPU Hardware. Second order scheme an...
Advances in the Solution of NS Eqs. in GPGPU Hardware. Second order scheme an...Advances in the Solution of NS Eqs. in GPGPU Hardware. Second order scheme an...
Advances in the Solution of NS Eqs. in GPGPU Hardware. Second order scheme an...Storti Mario
 

Was ist angesagt? (19)

Sk7 ph
Sk7 phSk7 ph
Sk7 ph
 
Chapter 2
Chapter 2Chapter 2
Chapter 2
 
Lecture13
Lecture13Lecture13
Lecture13
 
Green Telecom & IT Workshop: Marceau greentouch
Green Telecom & IT Workshop: Marceau greentouchGreen Telecom & IT Workshop: Marceau greentouch
Green Telecom & IT Workshop: Marceau greentouch
 
4 stochastic processes
4 stochastic processes4 stochastic processes
4 stochastic processes
 
Power Quality Monitoring by Disturbance Detection using Hilbert Phase Shifting
Power Quality Monitoring by Disturbance Detection using Hilbert Phase ShiftingPower Quality Monitoring by Disturbance Detection using Hilbert Phase Shifting
Power Quality Monitoring by Disturbance Detection using Hilbert Phase Shifting
 
Introduction of GPS BPSK-R and BOC
Introduction of GPS BPSK-R and BOCIntroduction of GPS BPSK-R and BOC
Introduction of GPS BPSK-R and BOC
 
Ppt of analog communication
Ppt of analog communicationPpt of analog communication
Ppt of analog communication
 
DSP_FOEHU - Lec 03 - Sampling of Continuous Time Signals
DSP_FOEHU - Lec 03 - Sampling of Continuous Time SignalsDSP_FOEHU - Lec 03 - Sampling of Continuous Time Signals
DSP_FOEHU - Lec 03 - Sampling of Continuous Time Signals
 
Circuit Network Analysis - [Chapter2] Sinusoidal Steady-state Analysis
Circuit Network Analysis - [Chapter2] Sinusoidal Steady-state AnalysisCircuit Network Analysis - [Chapter2] Sinusoidal Steady-state Analysis
Circuit Network Analysis - [Chapter2] Sinusoidal Steady-state Analysis
 
Design and Implementation of Low Ripple Low Power Digital Phase-Locked Loop
Design and Implementation of Low Ripple Low Power Digital Phase-Locked LoopDesign and Implementation of Low Ripple Low Power Digital Phase-Locked Loop
Design and Implementation of Low Ripple Low Power Digital Phase-Locked Loop
 
Matlab implementation of fast fourier transform
Matlab implementation of  fast fourier transformMatlab implementation of  fast fourier transform
Matlab implementation of fast fourier transform
 
Signal & system
Signal & systemSignal & system
Signal & system
 
Autoregression
AutoregressionAutoregression
Autoregression
 
Toward an Improved Computational Strategy for Vibration-Proof Structures Equi...
Toward an Improved Computational Strategy for Vibration-Proof Structures Equi...Toward an Improved Computational Strategy for Vibration-Proof Structures Equi...
Toward an Improved Computational Strategy for Vibration-Proof Structures Equi...
 
Filter-Type Fault Detection and Exclusion (FDE) on Multi-Frequency GNSS Receiver
Filter-Type Fault Detection and Exclusion (FDE) on Multi-Frequency GNSS ReceiverFilter-Type Fault Detection and Exclusion (FDE) on Multi-Frequency GNSS Receiver
Filter-Type Fault Detection and Exclusion (FDE) on Multi-Frequency GNSS Receiver
 
Introduction to Communication Systems 2
Introduction to Communication Systems 2Introduction to Communication Systems 2
Introduction to Communication Systems 2
 
IVR - Chapter 3 - Basics of filtering II: Spectral filters
IVR - Chapter 3 - Basics of filtering II: Spectral filtersIVR - Chapter 3 - Basics of filtering II: Spectral filters
IVR - Chapter 3 - Basics of filtering II: Spectral filters
 
Advances in the Solution of NS Eqs. in GPGPU Hardware. Second order scheme an...
Advances in the Solution of NS Eqs. in GPGPU Hardware. Second order scheme an...Advances in the Solution of NS Eqs. in GPGPU Hardware. Second order scheme an...
Advances in the Solution of NS Eqs. in GPGPU Hardware. Second order scheme an...
 

Ähnlich wie 20120214 optical pulse_measurement_wei-yi

The vibration error of the fiber optic gyroscope rotation rate and methods of...
The vibration error of the fiber optic gyroscope rotation rate and methods of...The vibration error of the fiber optic gyroscope rotation rate and methods of...
The vibration error of the fiber optic gyroscope rotation rate and methods of...Kurbatov Roman
 
Mining of time series data base using fuzzy neural information systems
Mining of time series data base using fuzzy neural information systemsMining of time series data base using fuzzy neural information systems
Mining of time series data base using fuzzy neural information systemsDr.MAYA NAYAK
 
LeastSquaresParameterEstimation.ppt
LeastSquaresParameterEstimation.pptLeastSquaresParameterEstimation.ppt
LeastSquaresParameterEstimation.pptStavrovDule2
 
Digital Signal Processing Tutorial:Chapt 3 frequency analysis
Digital Signal Processing Tutorial:Chapt 3 frequency analysisDigital Signal Processing Tutorial:Chapt 3 frequency analysis
Digital Signal Processing Tutorial:Chapt 3 frequency analysisChandrashekhar Padole
 
MATHEMATICAL MODELING OF COMPLEX REDUNDANT SYSTEM UNDER HEAD-OF-LINE REPAIR
MATHEMATICAL MODELING OF COMPLEX REDUNDANT SYSTEM UNDER HEAD-OF-LINE REPAIRMATHEMATICAL MODELING OF COMPLEX REDUNDANT SYSTEM UNDER HEAD-OF-LINE REPAIR
MATHEMATICAL MODELING OF COMPLEX REDUNDANT SYSTEM UNDER HEAD-OF-LINE REPAIREditor IJMTER
 
Implementation of adaptive stft algorithm for lfm signals
Implementation of adaptive stft algorithm for lfm signalsImplementation of adaptive stft algorithm for lfm signals
Implementation of adaptive stft algorithm for lfm signalseSAT Journals
 
Trends in Future CommunicationsInternational Workshop - Renato Rabelo
Trends in Future CommunicationsInternational Workshop - Renato RabeloTrends in Future CommunicationsInternational Workshop - Renato Rabelo
Trends in Future CommunicationsInternational Workshop - Renato RabeloCPqD
 
Nyquist criterion for zero ISI
Nyquist criterion for zero ISINyquist criterion for zero ISI
Nyquist criterion for zero ISIGunasekara Reddy
 
Prof A Giaralis, STOCHASTIC DYNAMICS AND MONTE CARLO SIMULATION IN EARTHQUAKE...
Prof A Giaralis, STOCHASTIC DYNAMICS AND MONTE CARLO SIMULATION IN EARTHQUAKE...Prof A Giaralis, STOCHASTIC DYNAMICS AND MONTE CARLO SIMULATION IN EARTHQUAKE...
Prof A Giaralis, STOCHASTIC DYNAMICS AND MONTE CARLO SIMULATION IN EARTHQUAKE...PhD ISG, Sapienza University of Rome
 
1 radar signal processing
1 radar signal processing1 radar signal processing
1 radar signal processingSolo Hermelin
 
Model reduction design for continuous systems with finite frequency specifications
Model reduction design for continuous systems with finite frequency specificationsModel reduction design for continuous systems with finite frequency specifications
Model reduction design for continuous systems with finite frequency specificationsIJECEIAES
 
Trunsored data analysis
Trunsored data analysisTrunsored data analysis
Trunsored data analysisHideo Hirose
 
10 range and doppler measurements in radar systems
10 range and doppler measurements in radar systems10 range and doppler measurements in radar systems
10 range and doppler measurements in radar systemsSolo Hermelin
 
Eece 301 note set 14 fourier transform
Eece 301 note set 14 fourier transformEece 301 note set 14 fourier transform
Eece 301 note set 14 fourier transformSandilya Sridhara
 

Ähnlich wie 20120214 optical pulse_measurement_wei-yi (20)

The vibration error of the fiber optic gyroscope rotation rate and methods of...
The vibration error of the fiber optic gyroscope rotation rate and methods of...The vibration error of the fiber optic gyroscope rotation rate and methods of...
The vibration error of the fiber optic gyroscope rotation rate and methods of...
 
CH3-1.pdf
CH3-1.pdfCH3-1.pdf
CH3-1.pdf
 
chap3.pptx
chap3.pptxchap3.pptx
chap3.pptx
 
Mining of time series data base using fuzzy neural information systems
Mining of time series data base using fuzzy neural information systemsMining of time series data base using fuzzy neural information systems
Mining of time series data base using fuzzy neural information systems
 
LeastSquaresParameterEstimation.ppt
LeastSquaresParameterEstimation.pptLeastSquaresParameterEstimation.ppt
LeastSquaresParameterEstimation.ppt
 
Digital Signal Processing Tutorial:Chapt 3 frequency analysis
Digital Signal Processing Tutorial:Chapt 3 frequency analysisDigital Signal Processing Tutorial:Chapt 3 frequency analysis
Digital Signal Processing Tutorial:Chapt 3 frequency analysis
 
MATHEMATICAL MODELING OF COMPLEX REDUNDANT SYSTEM UNDER HEAD-OF-LINE REPAIR
MATHEMATICAL MODELING OF COMPLEX REDUNDANT SYSTEM UNDER HEAD-OF-LINE REPAIRMATHEMATICAL MODELING OF COMPLEX REDUNDANT SYSTEM UNDER HEAD-OF-LINE REPAIR
MATHEMATICAL MODELING OF COMPLEX REDUNDANT SYSTEM UNDER HEAD-OF-LINE REPAIR
 
Implementation of adaptive stft algorithm for lfm signals
Implementation of adaptive stft algorithm for lfm signalsImplementation of adaptive stft algorithm for lfm signals
Implementation of adaptive stft algorithm for lfm signals
 
Trends in Future CommunicationsInternational Workshop - Renato Rabelo
Trends in Future CommunicationsInternational Workshop - Renato RabeloTrends in Future CommunicationsInternational Workshop - Renato Rabelo
Trends in Future CommunicationsInternational Workshop - Renato Rabelo
 
Nyquist criterion for zero ISI
Nyquist criterion for zero ISINyquist criterion for zero ISI
Nyquist criterion for zero ISI
 
Chapter 2
Chapter 2Chapter 2
Chapter 2
 
Prof A Giaralis, STOCHASTIC DYNAMICS AND MONTE CARLO SIMULATION IN EARTHQUAKE...
Prof A Giaralis, STOCHASTIC DYNAMICS AND MONTE CARLO SIMULATION IN EARTHQUAKE...Prof A Giaralis, STOCHASTIC DYNAMICS AND MONTE CARLO SIMULATION IN EARTHQUAKE...
Prof A Giaralis, STOCHASTIC DYNAMICS AND MONTE CARLO SIMULATION IN EARTHQUAKE...
 
Lecture 2 sapienza 2017
Lecture 2 sapienza 2017Lecture 2 sapienza 2017
Lecture 2 sapienza 2017
 
1 radar signal processing
1 radar signal processing1 radar signal processing
1 radar signal processing
 
Model reduction design for continuous systems with finite frequency specifications
Model reduction design for continuous systems with finite frequency specificationsModel reduction design for continuous systems with finite frequency specifications
Model reduction design for continuous systems with finite frequency specifications
 
Trunsored data analysis
Trunsored data analysisTrunsored data analysis
Trunsored data analysis
 
10 range and doppler measurements in radar systems
10 range and doppler measurements in radar systems10 range and doppler measurements in radar systems
10 range and doppler measurements in radar systems
 
4. cft
4. cft4. cft
4. cft
 
Eece 301 note set 14 fourier transform
Eece 301 note set 14 fourier transformEece 301 note set 14 fourier transform
Eece 301 note set 14 fourier transform
 
Chapter5
Chapter5Chapter5
Chapter5
 

Kürzlich hochgeladen

presentation ICT roal in 21st century education
presentation ICT roal in 21st century educationpresentation ICT roal in 21st century education
presentation ICT roal in 21st century educationjfdjdjcjdnsjd
 
Axa Assurance Maroc - Insurer Innovation Award 2024
Axa Assurance Maroc - Insurer Innovation Award 2024Axa Assurance Maroc - Insurer Innovation Award 2024
Axa Assurance Maroc - Insurer Innovation Award 2024The Digital Insurer
 
Exploring the Future Potential of AI-Enabled Smartphone Processors
Exploring the Future Potential of AI-Enabled Smartphone ProcessorsExploring the Future Potential of AI-Enabled Smartphone Processors
Exploring the Future Potential of AI-Enabled Smartphone Processorsdebabhi2
 
Artificial Intelligence: Facts and Myths
Artificial Intelligence: Facts and MythsArtificial Intelligence: Facts and Myths
Artificial Intelligence: Facts and MythsJoaquim Jorge
 
Polkadot JAM Slides - Token2049 - By Dr. Gavin Wood
Polkadot JAM Slides - Token2049 - By Dr. Gavin WoodPolkadot JAM Slides - Token2049 - By Dr. Gavin Wood
Polkadot JAM Slides - Token2049 - By Dr. Gavin WoodJuan lago vázquez
 
From Event to Action: Accelerate Your Decision Making with Real-Time Automation
From Event to Action: Accelerate Your Decision Making with Real-Time AutomationFrom Event to Action: Accelerate Your Decision Making with Real-Time Automation
From Event to Action: Accelerate Your Decision Making with Real-Time AutomationSafe Software
 
Boost Fertility New Invention Ups Success Rates.pdf
Boost Fertility New Invention Ups Success Rates.pdfBoost Fertility New Invention Ups Success Rates.pdf
Boost Fertility New Invention Ups Success Rates.pdfsudhanshuwaghmare1
 
Bajaj Allianz Life Insurance Company - Insurer Innovation Award 2024
Bajaj Allianz Life Insurance Company - Insurer Innovation Award 2024Bajaj Allianz Life Insurance Company - Insurer Innovation Award 2024
Bajaj Allianz Life Insurance Company - Insurer Innovation Award 2024The Digital Insurer
 
Deploy with confidence: VMware Cloud Foundation 5.1 on next gen Dell PowerEdg...
Deploy with confidence: VMware Cloud Foundation 5.1 on next gen Dell PowerEdg...Deploy with confidence: VMware Cloud Foundation 5.1 on next gen Dell PowerEdg...
Deploy with confidence: VMware Cloud Foundation 5.1 on next gen Dell PowerEdg...Principled Technologies
 
Cloud Frontiers: A Deep Dive into Serverless Spatial Data and FME
Cloud Frontiers:  A Deep Dive into Serverless Spatial Data and FMECloud Frontiers:  A Deep Dive into Serverless Spatial Data and FME
Cloud Frontiers: A Deep Dive into Serverless Spatial Data and FMESafe Software
 
Bajaj Allianz Life Insurance Company - Insurer Innovation Award 2024
Bajaj Allianz Life Insurance Company - Insurer Innovation Award 2024Bajaj Allianz Life Insurance Company - Insurer Innovation Award 2024
Bajaj Allianz Life Insurance Company - Insurer Innovation Award 2024The Digital Insurer
 
Repurposing LNG terminals for Hydrogen Ammonia: Feasibility and Cost Saving
Repurposing LNG terminals for Hydrogen Ammonia: Feasibility and Cost SavingRepurposing LNG terminals for Hydrogen Ammonia: Feasibility and Cost Saving
Repurposing LNG terminals for Hydrogen Ammonia: Feasibility and Cost SavingEdi Saputra
 
Mastering MySQL Database Architecture: Deep Dive into MySQL Shell and MySQL R...
Mastering MySQL Database Architecture: Deep Dive into MySQL Shell and MySQL R...Mastering MySQL Database Architecture: Deep Dive into MySQL Shell and MySQL R...
Mastering MySQL Database Architecture: Deep Dive into MySQL Shell and MySQL R...Miguel Araújo
 
MINDCTI Revenue Release Quarter One 2024
MINDCTI Revenue Release Quarter One 2024MINDCTI Revenue Release Quarter One 2024
MINDCTI Revenue Release Quarter One 2024MIND CTI
 
The 7 Things I Know About Cyber Security After 25 Years | April 2024
The 7 Things I Know About Cyber Security After 25 Years | April 2024The 7 Things I Know About Cyber Security After 25 Years | April 2024
The 7 Things I Know About Cyber Security After 25 Years | April 2024Rafal Los
 
Apidays New York 2024 - The Good, the Bad and the Governed by David O'Neill, ...
Apidays New York 2024 - The Good, the Bad and the Governed by David O'Neill, ...Apidays New York 2024 - The Good, the Bad and the Governed by David O'Neill, ...
Apidays New York 2024 - The Good, the Bad and the Governed by David O'Neill, ...apidays
 
Apidays New York 2024 - Scaling API-first by Ian Reasor and Radu Cotescu, Adobe
Apidays New York 2024 - Scaling API-first by Ian Reasor and Radu Cotescu, AdobeApidays New York 2024 - Scaling API-first by Ian Reasor and Radu Cotescu, Adobe
Apidays New York 2024 - Scaling API-first by Ian Reasor and Radu Cotescu, Adobeapidays
 
A Domino Admins Adventures (Engage 2024)
A Domino Admins Adventures (Engage 2024)A Domino Admins Adventures (Engage 2024)
A Domino Admins Adventures (Engage 2024)Gabriella Davis
 
Data Cloud, More than a CDP by Matt Robison
Data Cloud, More than a CDP by Matt RobisonData Cloud, More than a CDP by Matt Robison
Data Cloud, More than a CDP by Matt RobisonAnna Loughnan Colquhoun
 
A Year of the Servo Reboot: Where Are We Now?
A Year of the Servo Reboot: Where Are We Now?A Year of the Servo Reboot: Where Are We Now?
A Year of the Servo Reboot: Where Are We Now?Igalia
 

Kürzlich hochgeladen (20)

presentation ICT roal in 21st century education
presentation ICT roal in 21st century educationpresentation ICT roal in 21st century education
presentation ICT roal in 21st century education
 
Axa Assurance Maroc - Insurer Innovation Award 2024
Axa Assurance Maroc - Insurer Innovation Award 2024Axa Assurance Maroc - Insurer Innovation Award 2024
Axa Assurance Maroc - Insurer Innovation Award 2024
 
Exploring the Future Potential of AI-Enabled Smartphone Processors
Exploring the Future Potential of AI-Enabled Smartphone ProcessorsExploring the Future Potential of AI-Enabled Smartphone Processors
Exploring the Future Potential of AI-Enabled Smartphone Processors
 
Artificial Intelligence: Facts and Myths
Artificial Intelligence: Facts and MythsArtificial Intelligence: Facts and Myths
Artificial Intelligence: Facts and Myths
 
Polkadot JAM Slides - Token2049 - By Dr. Gavin Wood
Polkadot JAM Slides - Token2049 - By Dr. Gavin WoodPolkadot JAM Slides - Token2049 - By Dr. Gavin Wood
Polkadot JAM Slides - Token2049 - By Dr. Gavin Wood
 
From Event to Action: Accelerate Your Decision Making with Real-Time Automation
From Event to Action: Accelerate Your Decision Making with Real-Time AutomationFrom Event to Action: Accelerate Your Decision Making with Real-Time Automation
From Event to Action: Accelerate Your Decision Making with Real-Time Automation
 
Boost Fertility New Invention Ups Success Rates.pdf
Boost Fertility New Invention Ups Success Rates.pdfBoost Fertility New Invention Ups Success Rates.pdf
Boost Fertility New Invention Ups Success Rates.pdf
 
Bajaj Allianz Life Insurance Company - Insurer Innovation Award 2024
Bajaj Allianz Life Insurance Company - Insurer Innovation Award 2024Bajaj Allianz Life Insurance Company - Insurer Innovation Award 2024
Bajaj Allianz Life Insurance Company - Insurer Innovation Award 2024
 
Deploy with confidence: VMware Cloud Foundation 5.1 on next gen Dell PowerEdg...
Deploy with confidence: VMware Cloud Foundation 5.1 on next gen Dell PowerEdg...Deploy with confidence: VMware Cloud Foundation 5.1 on next gen Dell PowerEdg...
Deploy with confidence: VMware Cloud Foundation 5.1 on next gen Dell PowerEdg...
 
Cloud Frontiers: A Deep Dive into Serverless Spatial Data and FME
Cloud Frontiers:  A Deep Dive into Serverless Spatial Data and FMECloud Frontiers:  A Deep Dive into Serverless Spatial Data and FME
Cloud Frontiers: A Deep Dive into Serverless Spatial Data and FME
 
Bajaj Allianz Life Insurance Company - Insurer Innovation Award 2024
Bajaj Allianz Life Insurance Company - Insurer Innovation Award 2024Bajaj Allianz Life Insurance Company - Insurer Innovation Award 2024
Bajaj Allianz Life Insurance Company - Insurer Innovation Award 2024
 
Repurposing LNG terminals for Hydrogen Ammonia: Feasibility and Cost Saving
Repurposing LNG terminals for Hydrogen Ammonia: Feasibility and Cost SavingRepurposing LNG terminals for Hydrogen Ammonia: Feasibility and Cost Saving
Repurposing LNG terminals for Hydrogen Ammonia: Feasibility and Cost Saving
 
Mastering MySQL Database Architecture: Deep Dive into MySQL Shell and MySQL R...
Mastering MySQL Database Architecture: Deep Dive into MySQL Shell and MySQL R...Mastering MySQL Database Architecture: Deep Dive into MySQL Shell and MySQL R...
Mastering MySQL Database Architecture: Deep Dive into MySQL Shell and MySQL R...
 
MINDCTI Revenue Release Quarter One 2024
MINDCTI Revenue Release Quarter One 2024MINDCTI Revenue Release Quarter One 2024
MINDCTI Revenue Release Quarter One 2024
 
The 7 Things I Know About Cyber Security After 25 Years | April 2024
The 7 Things I Know About Cyber Security After 25 Years | April 2024The 7 Things I Know About Cyber Security After 25 Years | April 2024
The 7 Things I Know About Cyber Security After 25 Years | April 2024
 
Apidays New York 2024 - The Good, the Bad and the Governed by David O'Neill, ...
Apidays New York 2024 - The Good, the Bad and the Governed by David O'Neill, ...Apidays New York 2024 - The Good, the Bad and the Governed by David O'Neill, ...
Apidays New York 2024 - The Good, the Bad and the Governed by David O'Neill, ...
 
Apidays New York 2024 - Scaling API-first by Ian Reasor and Radu Cotescu, Adobe
Apidays New York 2024 - Scaling API-first by Ian Reasor and Radu Cotescu, AdobeApidays New York 2024 - Scaling API-first by Ian Reasor and Radu Cotescu, Adobe
Apidays New York 2024 - Scaling API-first by Ian Reasor and Radu Cotescu, Adobe
 
A Domino Admins Adventures (Engage 2024)
A Domino Admins Adventures (Engage 2024)A Domino Admins Adventures (Engage 2024)
A Domino Admins Adventures (Engage 2024)
 
Data Cloud, More than a CDP by Matt Robison
Data Cloud, More than a CDP by Matt RobisonData Cloud, More than a CDP by Matt Robison
Data Cloud, More than a CDP by Matt Robison
 
A Year of the Servo Reboot: Where Are We Now?
A Year of the Servo Reboot: Where Are We Now?A Year of the Servo Reboot: Where Are We Now?
A Year of the Servo Reboot: Where Are We Now?
 

20120214 optical pulse_measurement_wei-yi

  • 1. Introduction optical pulse measurement & Fiber clear Wei-Yi Tsai Institute of Photonics Technologies National Tsing Hua University, Taiwan Feb,14, 2011
  • 2. NTHU Outline  Defined of ultrafast  Mathematic introduce  definition  Correlation & Convolution  Pulse measurement methods  Field autocorrelation  Cross correlation  Intensity autocorrelation  Homework 2
  • 3. NTHU Defined of ultrafast  What is ultrafast ?  The range of ultrafast ? ‘’ ultrashort’’ refers to the femtosecond(fs) to picosecond(ps) range. 3 Milli- Micro- Nano- Pico- Femto- Atto- Time(s) 10e-3 10e-6 10e-9 10e-12 10e-15 10e-18 frequency 1kHz 1MHz 1GHz 1THz 1PHz 1EHz
  • 4. NTHU Goal of pulse measurement 4 * ( )1 ( ) Re{ ( ) } { ( ) ( ) }, ( ) ( ) 2 o o oj t j t j t j t E t a t e a t e a t e a t a t e           -10 -8 -6 -4 -2 0 2 4 6 8 10 -1 -0.8 -0.6 -0.4 -0.2 0 0.2 0.4 0.6 0.8 1 X: 0.5811 Y: 0.6262 ( )t ( )a t It is straightforward to get carrier frequency by spectrometer, we focus on measuring the complex envelope function
  • 5. NTHU Difficult  The laser pulse duration cannot be easily measured by optoelectronic methods, since the response time of phtodetector and oscilloscopes are at best of the order of 200(fs) 5(Shang-Da Yang, Ultrafast Optics, Lecture slide 05)
  • 6. NTHU Definition  For a given power spectrum , the pulse is :  Transform-limited (TL), if  Chirped, if is nonlinear 6 2 ( )A  ( ) 0   ( )  -10 -8 -6 -4 -2 0 2 4 6 8 10 -1 -0.8 -0.6 -0.4 -0.2 0 0.2 0.4 0.6 0.8 1 -10 -8 -6 -4 -2 0 2 4 6 8 10 -1 -0.8 -0.6 -0.4 -0.2 0 0.2 0.4 0.6 0.8 1 Chirped TL
  • 7. NTHU Pulse measurement method  Because the pulses are so short that no existing electronics are capable of resolving them, so the common approach is to measure the ultrashort pulse by itself  Auto-correlation  Cross-correlation 7 * 12 1 2( ) ( )f a t a t dt     * ( ) ( )f a t a t dt    
  • 8. NTHU Outline  Defined of ultrafast  Mathematic introduce  Correlation & Convolution  Pulse measurement methods  Field autocorrelation  Cross correlation  Intensity autocorrelation  FAQ 8
  • 9. NTHU Field autocorrelation 9(Shang-Da Yang, Ultrafast Optics, Lecture slide 05)
  • 10. NTHU Field autocorrelation trace formula 10 0 1( ) ( ) 1 Re{ ( ) }j FA outI P G e        11 01 ( ) cos( ( ))GG R        when 1 * ( ) 1 12 ( ) ( ) ( ) ( ) ( ) Gja t a t G G e C a t         Is the normalized field autocorrelation function of ( )a t
  • 11. NTHU Example A TL pulse with two smallside lobes 11(Shang-Da Yang, Ultrafast Optics, Lecture slide 05)
  • 12. NTHU How to retrieve G1 from the field autocorrelation  Perform Fourier transform for trace:  Extract the component centered at :  Shift to the baseband:  Perform inverse Fourier transform: 12 { ( )}FA FAI F I  , 0( ) ( )oFA FAI I      0,0 , 0( ) ( )FA FAI I        1 ,01( ) { ( )}FAG F I    11 0( ) 1 ( ) cos( ( ))FA GI G        (Shang-Da Yang, Ultrafast Optics, Lecture slide 05)
  • 13. NTHU Limitation  FA function is nothing but power spectrum of the field envelope a(t):  As a result  NO spectral phase information , then we cannot distinguish transform-limited pulse with long chirped pulse with and even incoherent noise 13 2 1{ ( )} ( )F G A  ( )  ( )TLI t ( ) 0   ( )chirpI t 2 2 ( ) 2      ( )noiseI t
  • 14. NTHU Limitation 14 -5 -4 -3 -2 -1 0 1 2 3 4 5 0 0.5 1 1.5 2 2.5 3 Temporal intensity profile Time t Intensity(a.u) -5 -4 -3 -2 -1 0 1 2 3 4 5 0 0.2 0.4 0.6 0.8 1 1.2 1.4 1.6 1.8 2 Field autocorrelation trace  -5 -4 -3 -2 -1 0 1 2 3 4 5 0 1 2 3 4 5 6 7 Temporal intensity profile Time t Intensity(a.u) -5 -4 -3 -2 -1 0 1 2 3 4 5 0 0.2 0.4 0.6 0.8 1 1.2 1.4 1.6 1.8 2 Field autocorrelation trace 
  • 15. NTHU Limitation 15 -5 -4 -3 -2 -1 0 1 2 3 4 5 0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 Temporal intensity profile Time t Intensity(a.u) -5 -4 -3 -2 -1 0 1 2 3 4 5 0 0.2 0.4 0.6 0.8 1 1.2 1.4 1.6 1.8 2 Field autocorrelation trace  11 0( ) 1 ( ) cos( ( ))FA GI G R        
  • 16. NTHU limitations  NO pulse asymmetry information, for 16 ( ) ( )FA FAI I   (Shang-Da Yang, Ultrafast Optics, Lecture slide 05)
  • 17. NTHU Outline  Defined of ultrafast  Mathematic introduce  Correlation & Convolution  Pulse measurement methods  Field autocorrelation  Cross correlation  Intensity autocorrelation  FAQ 17
  • 19. NTHU Field-cross-correlation  The field cross-correlation function of and 19 0 1,( ) ( ) 2Re{ ( ) }j Fx out s r xI P U U G e         1, 0 1,2 ( ) cos( ( ))tot x G xU G        * 1, ( ) ( ) ( )x s rG a t a t C    ( )sa t ( )ra t 2 ( )i iT U a t dt 
  • 20. NTHU Field cross-correlation  For very short reference pulse 20 r st t  0 1,2 ( ) cos( ( ))tot s G xU a        1, 0 1,( ) 2 ( ) cos( ( ))FX tot x G xI U G        1, ( ) ( ) ( ) ( )x s sG a t t a     
  • 21. NTHU Field cross-correlation  Perform Fourier transform for the trace  Extract the component centered at  Shift to the baseband 21 0 1,( ) 2Re ( ) j FX tot xI U G e      * 1, ( ) ( ) ( )x s rG a t a t   { ( )} ( )FXFXF I I    * * 0 0 0 0( ) [ ( ) ( ) ( ) ( )]s r s rA A A A                0 0, ( )FX oI    0 * ,0 , 0( ) ( ) ( ) ( )FX FX s rI I A A         
  • 22. NTHU Field cross-correlation  The exact complex spectrum of the signal pulse can be derived by:  If the complex spectrum of the reference pulse is known  Bandwidth of the reference pulse is broader than that of the signal pulse 22 ,0 * ( ) ( ) Fx s r I A A     ( )rA 
  • 23. NTHU Poor signal-to-background contrast 23 Cross-correlation Field-autocorrelation 0 1,( ) ( ) 2Re{ ( ) }j Fx out s r xI P U U G e         (Assume: TL Gaussian, ),s r s rU U t t   -10 -8 -6 -4 -2 0 2 4 6 8 10 0.7 0.8 0.9 1 1.1 1.2 1.3 1.4  cross-correlation -10 -8 -6 -4 -2 0 2 4 6 8 10 0 0.2 0.4 0.6 0.8 1 1.2 1.4 1.6 1.8 2  Field-autocorrelation
  • 24. NTHU Outline  Defined of ultrafast  Mathematic introduce  Correlation & Convolution  Pulse measurement methods  Field autocorrelation  Cross correlation  Intensity autocorrelation  FAQ 24
  • 25. NTHU Second harmonic generation (SHG) 25 NLO material 0 02 -1 -0.8 -0.6 -0.4 -0.2 0 0.2 0.4 0.6 0.8 1 x 10 -9 -1 -0.8 -0.6 -0.4 -0.2 0 0.2 0.4 0.6 0.8 1 -1 -0.8 -0.6 -0.4 -0.2 0 0.2 0.4 0.6 0.8 1 x 10 -9 -1 -0.8 -0.6 -0.4 -0.2 0 0.2 0.4 0.6 0.8 1 2 2 ( )j t j t e e   2 2 ( )a a t 
  • 27. NTHU Fringe-resolved intensity autocorrelation   …Intensity autocorrelation  ……Intensity-field correlation  ……Squared-field autocorrelation 27 2 0 0( ) 1 2 ( ) 4 ( ) cos( ( )) ( ) cos(2 ( ))FRIA f gI G f g                 2 2 ( ) ( ) ( ) ( ) I t I t G R I t      * 2 [ ( ) ( )] ( ) ( ) ( ) 2 ( ) I t I t a t a t f C I t         * 2 2 [ ( ) ( )] ( ) ( ) a t a t g C I t     
  • 28. NTHU Comparison between TL &chirped pulses of the same I(t) 28(Shang-Da Yang, Ultrafast Optics, Lecture slide 05)
  • 29. NTHU How to retrieve G2 from the Intensity autocorrelation  Perform Fourier transform for trace:  Extract the component centered at :  Remove the Dirac-function component  Perform inverse Fourier transform: 29 { ( )}FAIA FAIAI F I  ,0 ( ) ( 0)FRIA FRIAI I    1 ,02 ( ) { ( )}FRIAG F I    (Shang-Da Yang, Ultrafast Optics, Lecture slide 05)
  • 30. NTHU Intensity autocorrelation trace 30 -1 -0.8 -0.6 -0.4 -0.2 0 0.2 0.4 0.6 0.8 1 x 10 -13 0 1 2 3 4 5 6 7 8 Ifria  -2 -1.5 -1 -0.5 0 0.5 1 1.5 2 x 10 16 -0.5 0 0.5 1 1.5 2 2.5 3 x 10 -13 2 ( )G  -2 -1.5 -1 -0.5 0 0.5 1 1.5 2 x 10 16 -0.5 0 0.5 1 1.5 2 2.5 3 x 10 -13 X: 0 Y: 2.755e-013
  • 31. NTHU Intensity autocorrelation trace 31 -1 -0.8 -0.6 -0.4 -0.2 0 0.2 0.4 0.6 0.8 1 x 10 -13 0 1 2 3 4 5 6 7 8 Intensity autocorrelation trace  -1 -0.8 -0.6 -0.4 -0.2 0 0.2 0.4 0.6 0.8 1 x 10 -13 0 1 2 3 4 5 6 7 8 Intensity autocorrelation trace  2 ( )G 
  • 32. NTHU Deconvolution factor I(t) 1.41 1.54 1 32 decR 2 2( / )pt t e  2 sec ( / )ph t t ( / )pt t If this factor is know, or assumed, the time duration (Intensity width) of a pulse can be measured using an Intensity autocorrelation The deconvolution factor, defined as: /decR t  
  • 33. NTHU limitation  , no pulse asymmetry information. 33 2 2( ) ( )G G   (Shang-Da Yang, Ultrafast Optics, Lecture slide 05)
  • 34. NTHU Noncollinear 34 0(2) 2 ( , ) ( ) ( ) j a t a t a t e         2 (3) 2 2( ) ( , ) ( ) ( ) ( )IAI a t dt I t I t G       (Shang-Da Yang, Ultrafast Optics, Lecture slide 05)
  • 35. NTHU Homework  試著利用 一個Gaussian pulse 做field -auto-correlation, 並畫出 . 35 FAI
  • 36. NTHU Fiber 材質種類 36 Single mode fiber Polarization maintain fiber Dispersion compensate/increasing fiber
  • 38. NTHU Fiber 分類 & Different fiber patch cord connectors. 38PC APC
  • 39. NTHU Fiber clear  Step1 先用棉花棒沾酒精  Step2 再用棉花棒沿著fiber頭的面積擦一次  Step3 最後用氮氣槍,把酒精吹乾 39