SlideShare una empresa de Scribd logo
1 de 20
Descargar para leer sin conexión
Lecture 2: Photodetection and
      Photodetectors




ECE228B, Prof. D. J. Blumenthal       Lecture 2, Slide 1
Photodetection (Continued)




ECE228B, Prof. D. J. Blumenthal   Lecture 2, Slide 2
Electrical Signal-to-Noise Ratio (SNR)
  ➥ At the receiver, there is noise on the signal arriving at the input and and after detection
  added to that is noise that is injected at various stages of the receiver
       ➥ The current output of the receiver in(t) has current contributions from
             ➥ Electrical shot noise
             ➥ Thermal noise
             ➥ APD detectors have additional multiplication noise
             ➥ Amplifier noise                      photodetector

                                       Popt(t) = PSig(t) + Pn(t)              I(t) = Ip(t) + in(t)
                                                                   Receiver
         Detector Output Current (I)




                                                                                2σ1

                                       <I1>


                                       <I0>
                                                                                 2σ0
                                                                                            t
ECE228B, Prof. D. J. Blumenthal                                                                 Lecture 2, Slide 3
Modeling Detector SNR
   When observing the detector current output, it is difficult to tell which noise was
    present at the optical input and which noise was generated internal to the detector.
    So we tend to use several different models and combine them


Optical signal = DC          Current = DC                              Current = DC                     Current = DC
component +                  component +                               component +                      component +
variance (Poisson            variance (Poisson                         variance (Filtered               variance (Filtered
Process)                     Process)                                  Poisson Process)                 Poisson Process +
                                                 Hd(ω) = FT {hd (t)}                                    additive noise)
                                                                                             !
                    Ideal
                photodetector                        Filtering

                                                                                            Internal
                                                                                            Detector
                                                                                             Noise




ECE228B, Prof. D. J. Blumenthal                                                                        Lecture 2, Slide 4
Noise Current
   To quantify the statistical nature of noise, we can’t determine random events ahead
    of time, but we can use their “spectral” characteristics to quantify statistical
    behavior
         Define an Average (mean) value to quantify the amount of power (energy) in the non-
          time varying part of the signal
         Define a Variance to quantify the amount of power (energy) in the noisy part of the
          signal
   Define the “noise” current as


                                  i(t) = I DC + inoise (t)




ECE228B, Prof. D. J. Blumenthal                                                 Lecture 2, Slide 5
Shot Noise Mean and Variance
 For constant power illumination, the rate parameter is constant, and the signal is the mean
 The noise corresponds to the photocurrent variance
 For a filter, homogeneous Poisson process
                                                                t
                                                      !q
         Mean (Amps)                  is (t) = i(t) =    Precvd $ hd (# )d#            • Both mean and variance are
                                                      h"        0                      linear with Prevd
                                                                     t                 • As Prcvd is increased, both
                                  2                   !q           2                   signal and noise increase
         Variance (Amps2)         i (t) = var{i(t)} =
                                  n                      Precvd $ hd (# )d#
                                                      h"        0
                       Power Spectrum

                             2
                           I DC                                          Total Shot Noise =
                                                                               Area = 2qI DC B
                2
               in (0) = 2qI DC

                                                                                             f
                                                                               B
                                                 Detector Bandwidth

ECE228B, Prof. D. J. Blumenthal                                                                      Lecture 2, Slide 6
Photodetector Shot Noise
  The shot noise generated in the photodetection process is physically due to the “quantum
   granularity” of the received (and photo converted) optical signal
  Shot noise sets the ultimate limit of an optical receiver
  Shot noise is a Poisson noise, but it is usually approximated as a Gaussian noise
  Hallmark of shot noise is dependence on q, the electron charge

              Constant Input Optical Power    Detector Shot Noise

              Optically induced current + random electron fluctuations
                                                                               σ2Shot = 2q(Ip + Id)Δf
      Popt                                                                           I

Pin                                Detector                                       <Ip>
                                  (BW = Δf)
                                                      I(t) = <Ip> + ishot(t)
                                                                                                     2σshot
                   t                                                                             t
Poisson,
no-modulation
             Thermally induced current + random electron fluctuations

                    Dark Current (Id)         Detector Shot Noise
ECE228B, Prof. D. J. Blumenthal                                                             Lecture 2, Slide 7
Shot Noise with Data Modulation
      Consider how the picture changes when we have information modulated on the optical carrier
      Let m(t) be the information transmitted
             Then Prcvd (t) and λ(t) are functions of m(t)
             Assuming the photodetector filter impulse function can change in amplitude from time period to time
              period, let Gj be a time varying parameter
                                                        N
                                                i(t) = # G j hd (t ! " j )
                                                       j =1


                                                     Power Spectrum
                     t
                !
is (t) = i(t) =   G ' Prcvd (# )hd (t % # )d#
                                 $                         2
                                                         I DC                                     Total Shot Noise =
                h" %&                                                           Modulation              Area = 2qI DC B
                              t
2                   ! 2              2
                                                      2qI DC
i (t) = var{i(t)} =
n                     G & Prcvd (# )hd (t $ # )d#
                    h" $%
                                                                                                                          f
                                                                Modulation Bandwidth
                                                                                                         B
                                                                             Detector Bandwidth
    ECE228B, Prof. D. J. Blumenthal                                                                  Lecture 2, Slide 8
Ideal Direct Detection (1)

                                                                                        Ideal Amplifier = unity gain,
                                                                                        zero noise, equivalent load RL

          Ei(t)                      i’(t)        Hd(ω) = FT {hd(t)}                            AV=1
                                                                                 i(t)                                       Vout(t)
                          Ideal
                          Detector
                                                                                                  RL

                                                        # !q Ei (t) 2 &
                                             i(t) = LPF %             (
 Ei (t) = 2Ps Z 0 cos(! s t + " )                       % h" Z 0 (
                                                        $             '                              Vout (t) = i(t)RL
          # Ei (t) 2       &                             # !q 2Ps Z 0                    &
                                                 = LPF %              Cos 2 () s t + * ) (                               !q
          %                (                             $ h" Z 0                        '                  = idc RL =      Ps RL
  Pavg   =%     2          ( = Ps                                                                                        h"
          %          Z0    (                             # !q     1                        &
                                                 = LPF % 2Ps [1 + cos 2() s t + * )](
          %
          $                (
                           '                             $ h"     2                        '
                                                         !q
                                                 = idc =     Ps
                                                         h"


ECE228B, Prof. D. J. Blumenthal                                                                               Lecture 2, Slide 9
Ideal Direct Detection (2)
 Electrical SNR is found using the ratio between the signal power (DC) generated in the load
  resistor and the noise power (shot noise) generated in the load resistor


                                                                    2
                                                     # !q         &
                                                          Psignal (
                             Psignal    isignal RL % h"
                                          2
                                                     $            '   1 !Psignal
             SNRdd =                   = 2         =                =
                              Pnoise     inoise RL   2qh" Psignal B 2 h" B


 This equation shows the fundamental, quantum shot noise limit, where the SNR is limited
  only by the shot noise itself -> Shot Noise Limited Direct (Incoherent) Detection
 SNR improves linearly with input signal strength
 We will discuss other noise contributions that exist that make it difficult to reach this limit


ECE228B, Prof. D. J. Blumenthal                                                     Lecture 2, Slide 10
Ideal Coherent Detection (1)
 Consider the following ideal Heterodyne Coherent Receiver
          Heterodyne implies that a non-zero intermediate frequency (ωIF) is generated prior to data recovery

                         Elo(t)                                                                                  Ideal Amplifier = unity gain, zero
           Local                                                                                                 noise, equivalent load RL
           Oscillator                         ε Plo+ (1-ε) Psignal
                                       ε
                         Es(t)
                                                                     i’(t)       Hd(ω) = FT {hd(t)}                       AV=1
                                                 Ideal                                                    i(t)                                 Vout(t)
           Input                    Power
                                                 Detector
                                   Combiner
                                                                                                                            RL


                                                                  ' !q 1                                                                           2*
                                                       i(t) = LPF )             # 2Plo Z 0 cos($ lot + % ) + 1 & # 2Prcvd Z 0 cos($ s t + % ) ,
Ei (t) = ! Elo (t) + 1 " ! Es (t)                                 ( h" Z 0                                                                           +
                                                                               1                   1
        = ! 2Plo Z 0 cos(# lot + $ )                   using cos - cos . = cos(- & . ) + cos(- + . )
                                                                               2                   2
        + 1 " ! 2Prcvd Z 0 cos(# s t + $ )                         ' !q                                                                            *
                                                       i(t) = LPF )
                                                                   ( h"
                                                                             {                                              (           }
                                                                         Plo# + Prcvd (1 & # ) + 2 Plo Prcvd # (1 & # ) cos '($ s & $ lo ) t + % * ,
                                                                                                                                                 +
                                                                                                                                                   +
         % Ei (t) 2       (
         '                *                            Since typically Prcvd = Plo
 Pavg   ='     2          * = Ps                              !q
                    Z0                                 I DC ;    Plo#
         '                *                                   h"
         '
         &                *
                          )                            Assuming the intermmediate frequency ($ IF = $ s & $ lo ) falls within the LPF bandwidth
                                                              !q
                                                       i(t) =    2 Plo Prcvd # (1 & # ) cos [$ IF t + % ]
                                                              h"
ECE228B, Prof. D. J. Blumenthal                                                                                                     Lecture 2, Slide 11
Ideal Coherent Detection (2)
 Using the same approach as in direct detection to obtain the SNR
                                                                  2
                                                       ! i peak $

         SNRhet =
                    Psignal
                              = 2
                                 2
                               isignal RL
                                          =
                                            (irms )2 = # 2 &
                                                       "        %
                     Pnoise     inoise RL 2qI DC BRL 2qI DC BRL
                                               2
                   ! 'q                        $
                        2 Plo Prcvd ) (1 * ) )
                   # h(                        &
                   #            2              &
                   #
                   "                           &
                                               %   '(1 * ) )Prcvd 'Prcvd
                 =                               =               ;
                          ! 'q       $                 h( B        h( B     limit Plo +,, ) +0
                       2q # Plo) & BRL
                          " h(       %

  Note that shot noise limited heterodyne coherent detection, in the limit where the local
   oscillator is much stronger than the received signal,
         Is a factor of 2 (3dB) better than the shot noise limited incoherent detection


ECE228B, Prof. D. J. Blumenthal                                                                  Lecture 2, Slide 12
Ideal Coherent Detection (3)
 The other coherent approach is Homodyne Coherent Detection
           The intermediate frequency (ωIF) is driven to zero (ωIF=0) at phase is driven to φ=0 bringing the data
            immediately to baseband
                                                                                        Automatic
                                                                Local                                                     Ideal Amplifier = unity gain, zero
                                                                                     Frequency/Phase
                                                               Oscillator                                                 noise, equivalent load RL
               Elo(t)                                                                    Control
                                        ε Plo+ (1-ε) Psignal
                               ε
                Es(t)
                                                                 i’(t)             Hd(ω) = FT {hd(t)}                               AV=1
                                           Ideal                                                            i(t)                                             Vout(t)
 Input                     Power
                                           Detector
                          Combiner
                                                                                                                                       RL



                                                                          ' !q 1                                                                        2*
Ei (t) = ! Elo (t) + 1 " ! Es (t)                              i(t) = LPF )                # 2Plo Z 0 cos($ lot + % ) + 1 & # 2Prcvd Z 0 cos($ s t + % ) ,
                                                                          ( h" Z 0                                                                       +
         = ! 2Plo Z 0 cos(# lot + $ )
         + 1 " ! 2Prcvd Z 0 cos(# s t + $ )                    Since we are using an AFC/APC control to drive $ IF = 0
                                                                           ' !q                                                *
          % Ei (t) 2      (                                    i(t) = LPF )
                                                                           ( h"
                                                                                    {                                         }
                                                                                Plo# + Prcvd (1 & # ) + 2 Plo Prcvd # (1 & # ) ,
                                                                                                                               +
          '               *
 Pavg    ='     2         * = Ps                               Since typically Prcvd = Plo
                     Z0
          '               *                                           !q
          '               *                                    I DC ;    Plo#
          &               )                                           h"

                                                                         !q
                                                               i(t) =       2 Plo Prcvd # (1 & # )
ECE228B, Prof. D. J. Blumenthal                                          h"                                                                  Lecture 2, Slide 13
Ideal Coherent Detection (4)
 Using the same approach as in direct detection to obtain the SNR




       SNRhet =
                   Psignal
                             =
                                  2
                                 isignal RL
                                              =
                                                  (irms )2
                                  2
                   Pnoise        inoise RL        2qI DC B
                                                       2
                  % !q                     (
                  ' 2 Plo Prcvd # (1 $ # ) *
                  & h"                     )   ! 2(1 $ # )Prcvd    !P
                =                            =                  ; 2 rcvd
                          % !q     (                h" B           h" B       limit Plo +,, # +0
                       2q ' Plo# * B
                          & h"     )

  Note that shot noise limited homodyne coherent detection, in the limit where the local
   oscillator is much stronger than the received signal,
         Is a factor of 2 (3dB) better than the shot noise limited heterodyne receiver and factor of 4 (6dB)
          better than the shot noise limited incoherent detection



ECE228B, Prof. D. J. Blumenthal                                                                    Lecture 2, Slide 14
Photodetectors




ECE228B, Prof. D. J. Blumenthal   Lecture 2, Slide 15
Photoconductors (1)
 ➱ Photon absorption in semiconductor materials.
 ➱ Three main absorption mechanisms: Intrinsic (band-to-band), Free-Carrier Absorption and
 Band-and-Impurity Absorption
 ➱ Intrinsic (band-to-band) is the dominant effect in most SC photoconductors


       Intrinsic (band-to-band)                      Free-Carrier Absorption            Band-and-Impurity Absorption
                                                               e-
                      e-                   Ephoton = hν
                                  Ec                           h+        Ec                         e-           Ec
                                                                               Ephoton = hν
Ephoton = hν                                                                                        +           Donor Level
                                                                                                    -           Acceptor Level
                                                                               Ephoton = hν
                     h+           Ev                                     Ev                                      Ev
                                                                                                    h+

   •Incident photon Ephoton= hν= Ec - Ev




 ECE228B, Prof. D. J. Blumenthal                                                                         Lecture 2, Slide 16
Photoconductors (2)

     ➱ For intrinsic absorption, photons can be absorbed if
                                                   hc    1.24
                                   ! ( µ m) >          =
                                                Ec " EV Eg (eV )
                                                 1240
                                   ! (nm) >
                                                Eg (eV )


                  Material        Bandgap (eV)             Maximum λ (nm)   Typical Operating
                                                                              Range (nm)
             Si                       1.12                      1110            500-900

             Ge                       0.67                      1850            900-1300

             GaAs                     1.43                      870             750-850

             InxGa1-xAsyP1-y        0.38-2.25                 550-3260         1000-1600




ECE228B, Prof. D. J. Blumenthal                                                                 Lecture 2, Slide 17
Photoconductors (3)

           Ephoton = hν                  Semiconductor               ➱ Define:
                                                                         ➱ Pi = incident optical power
                                                                         ➱ R(λ) power reflectivity from input
                          Pi                                             medium to semiconductor
                          Pi(1-R)            Pi(1-R)e-αx                 ➱ α(λ) = 1/e absorption length
                                                                         ➱ 1/ α(λ) = penetration depth
                                                                 x
                                       1/α


   ➱ Power absorbed by the semiconductor is

                Pabs (x) = Pi (1 ! R)(1 ! e!" ( # )x )
                         = $(#, x)Pi

  ➱ defining the efficiency
                              number of photocarriers produced
                !(", x) =
                                number of incident photons
                        = (1 # R)(1 # e#$ ( " )x )
                      0 % !(", x) % 1

ECE228B, Prof. D. J. Blumenthal                                                                  Lecture 2, Slide 18
Photoconductive Photodetectors (1)
     Photogenerated current will have time and wavelength dependence
                            !q
               i photo (t) =    GPrcvd (t) + idark
                             h"                                                                                  Pi
               # carrier = mean free carrier lifetime
               # transit = transit time between eletrical contacts
                   $#          '
               G = & carrier ) = photoconductive gain
                   % # transit (
               idark = dark current




                                                                                             Metal




                                                                                                                      Metal
                                                                                                     Semiconductor

      The transit time for electrons and holes can be different and in many SCs the
       eletron mobility is greater than that of the hole

              ! e = µe E > µh E = ! h
                                                                                                                 +            iphoto
      The SC must remain charge neutral, for every electron generated, multiple holes
       will get pulled in until the photogenerated electron reaches the other contact. The
       carrier and transit times are limited by the slower carrier and the photoconductive                  Vbias
       gain is given by the ratio of the transit times
                            L
                 ! carrier = a
                            "h
                            L
                 ! transit = a
                            "e

    ECE228B, Prof. D. J. Blumenthal                                                                           Lecture 2, Slide 19
Photoconductive Photodetectors (2)

 The carrier velocity is a linear function of electric field strength up to a saturation
  velocity (which is the same for both electrons and holes)
       Field strength of about 105 V/cm result in velocities in range of 6x106 to 107 cm/s
       Some materials have an electron drift velocity that peaks at 2x107 cm/s at 104 V/cm
 When photoconductive gain is desirable, detector is operated at low voltages
 Carrier lifetime also impacts the frequency response of the photoconductive
  photodetector
                                                          Prcvd (! )
                                  i photo (! ) = "G
                                                                       2
                                                            #!&
                                                         1+ % (
                                                            $ !c '
                                            1
                                  !c =               = cutoff frequency
                                         ) carrier


ECE228B, Prof. D. J. Blumenthal                                                   Lecture 2, Slide 20

Más contenido relacionado

La actualidad más candente

Noise basics and its modelling
Noise basics and its modellingNoise basics and its modelling
Noise basics and its modellingDr Naim R Kidwai
 
Noise 2.0
Noise 2.0Noise 2.0
Noise 2.0bhavyaw
 
Understanding Noise Figure
Understanding Noise FigureUnderstanding Noise Figure
Understanding Noise FigureMostafa Ali
 
Communication Engineering - Chapter 6 - Noise
Communication Engineering - Chapter 6 - NoiseCommunication Engineering - Chapter 6 - Noise
Communication Engineering - Chapter 6 - Noisemkazree
 
Noise in Communication System
Noise in Communication SystemNoise in Communication System
Noise in Communication SystemIzah Asmadi
 
Instrumentation & Measurement: Noise and Its Types
Instrumentation & Measurement: Noise and Its TypesInstrumentation & Measurement: Noise and Its Types
Instrumentation & Measurement: Noise and Its TypesMuhammad Junaid Asif
 
Noise 2.0
Noise 2.0Noise 2.0
Noise 2.0bhavyaw
 
communication system Chapter 6
communication system Chapter 6communication system Chapter 6
communication system Chapter 6moeen khan afridi
 
System noise temperature @ Satellite Link Design
System noise temperature  @ Satellite Link Design System noise temperature  @ Satellite Link Design
System noise temperature @ Satellite Link Design AJAL A J
 
Theory Communication
Theory CommunicationTheory Communication
Theory CommunicationHikari Riten
 
Transmission impairments
Transmission impairmentsTransmission impairments
Transmission impairmentsavocado1111
 
Jhonson nyquist noise
Jhonson nyquist noiseJhonson nyquist noise
Jhonson nyquist noiseKartavya Jain
 
Signals and noise
Signals and noiseSignals and noise
Signals and noiseRavi Kant
 

La actualidad más candente (20)

Noise basics and its modelling
Noise basics and its modellingNoise basics and its modelling
Noise basics and its modelling
 
Noise
NoiseNoise
Noise
 
Noise 2.0
Noise 2.0Noise 2.0
Noise 2.0
 
Understanding Noise Figure
Understanding Noise FigureUnderstanding Noise Figure
Understanding Noise Figure
 
Communication Engineering - Chapter 6 - Noise
Communication Engineering - Chapter 6 - NoiseCommunication Engineering - Chapter 6 - Noise
Communication Engineering - Chapter 6 - Noise
 
Noise in Communication System
Noise in Communication SystemNoise in Communication System
Noise in Communication System
 
Types of noise
Types of noiseTypes of noise
Types of noise
 
Instrumentation & Measurement: Noise and Its Types
Instrumentation & Measurement: Noise and Its TypesInstrumentation & Measurement: Noise and Its Types
Instrumentation & Measurement: Noise and Its Types
 
Chapter 5 noise
Chapter 5 noiseChapter 5 noise
Chapter 5 noise
 
Noise 2.0
Noise 2.0Noise 2.0
Noise 2.0
 
Noise
NoiseNoise
Noise
 
communication system Chapter 6
communication system Chapter 6communication system Chapter 6
communication system Chapter 6
 
Noise
NoiseNoise
Noise
 
System noise temperature @ Satellite Link Design
System noise temperature  @ Satellite Link Design System noise temperature  @ Satellite Link Design
System noise temperature @ Satellite Link Design
 
Theory Communication
Theory CommunicationTheory Communication
Theory Communication
 
Transmission impairments
Transmission impairmentsTransmission impairments
Transmission impairments
 
Noise
NoiseNoise
Noise
 
Jhonson nyquist noise
Jhonson nyquist noiseJhonson nyquist noise
Jhonson nyquist noise
 
Signals and noise
Signals and noiseSignals and noise
Signals and noise
 
Noise analysis
Noise  analysisNoise  analysis
Noise analysis
 

Destacado

AFCCE AM Revitilization
AFCCE AM RevitilizationAFCCE AM Revitilization
AFCCE AM RevitilizationLawrence Behr
 
Lecture 1 introduction and signals analysis
Lecture 1 introduction and signals analysisLecture 1 introduction and signals analysis
Lecture 1 introduction and signals analysistalhawaqar
 
Noise in communication system
Noise in communication systemNoise in communication system
Noise in communication systemfirdous006
 
Lect12 photodiode detectors
Lect12 photodiode detectorsLect12 photodiode detectors
Lect12 photodiode detectorswtyru1989
 
Photo-detector by GIRISH HARMUKH
Photo-detector by GIRISH HARMUKHPhoto-detector by GIRISH HARMUKH
Photo-detector by GIRISH HARMUKHGIRISH HARMUKH
 

Destacado (7)

AFCCE AM Revitilization
AFCCE AM RevitilizationAFCCE AM Revitilization
AFCCE AM Revitilization
 
Lecture 1 introduction and signals analysis
Lecture 1 introduction and signals analysisLecture 1 introduction and signals analysis
Lecture 1 introduction and signals analysis
 
Noise in communication system
Noise in communication systemNoise in communication system
Noise in communication system
 
Lect12 photodiode detectors
Lect12 photodiode detectorsLect12 photodiode detectors
Lect12 photodiode detectors
 
Photo-detector by GIRISH HARMUKH
Photo-detector by GIRISH HARMUKHPhoto-detector by GIRISH HARMUKH
Photo-detector by GIRISH HARMUKH
 
Optical receivers
Optical receiversOptical receivers
Optical receivers
 
Photodetectors
PhotodetectorsPhotodetectors
Photodetectors
 

Similar a Photodetection and photodetectors

ECE375_Lec2_signal processing for engineering.pdf
ECE375_Lec2_signal processing for engineering.pdfECE375_Lec2_signal processing for engineering.pdf
ECE375_Lec2_signal processing for engineering.pdfssuserd36536
 
Unit 1 -Introduction to signals and standard signals
Unit 1 -Introduction to signals  and standard signalsUnit 1 -Introduction to signals  and standard signals
Unit 1 -Introduction to signals and standard signalsDr.SHANTHI K.G
 
Photo detector noise
Photo detector noisePhoto detector noise
Photo detector noiseGec bharuch
 
Cognitive radio spectrum sensing and performance evaluation of energy detecto...
Cognitive radio spectrum sensing and performance evaluation of energy detecto...Cognitive radio spectrum sensing and performance evaluation of energy detecto...
Cognitive radio spectrum sensing and performance evaluation of energy detecto...IAEME Publication
 
Cognitive radio spectrum sensing and performance evaluation of energy detecto...
Cognitive radio spectrum sensing and performance evaluation of energy detecto...Cognitive radio spectrum sensing and performance evaluation of energy detecto...
Cognitive radio spectrum sensing and performance evaluation of energy detecto...IAEME Publication
 
RF Module Design - [Chapter 3] Linearity
RF Module Design - [Chapter 3]  LinearityRF Module Design - [Chapter 3]  Linearity
RF Module Design - [Chapter 3] LinearitySimen Li
 
Ee443 phase locked loop - presentation - schwappach and brandy
Ee443   phase locked loop - presentation - schwappach and brandyEe443   phase locked loop - presentation - schwappach and brandy
Ee443 phase locked loop - presentation - schwappach and brandyLoren Schwappach
 
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
 
Optical Fiber Communication Part 3 Optical Digital Receiver
Optical Fiber Communication Part 3 Optical Digital ReceiverOptical Fiber Communication Part 3 Optical Digital Receiver
Optical Fiber Communication Part 3 Optical Digital ReceiverMadhumita Tamhane
 
PPT Chapter-1-V1.pptx__26715_1_1539251776000.pptx.pptx
PPT Chapter-1-V1.pptx__26715_1_1539251776000.pptx.pptxPPT Chapter-1-V1.pptx__26715_1_1539251776000.pptx.pptx
PPT Chapter-1-V1.pptx__26715_1_1539251776000.pptx.pptxidrissaeed
 
An automated and user-friendly optical tweezers for biomolecular investigat...
An automated and user-friendly optical  tweezers for biomolecular  investigat...An automated and user-friendly optical  tweezers for biomolecular  investigat...
An automated and user-friendly optical tweezers for biomolecular investigat...Dr. Pranav Rathi
 
Correlative level coding
Correlative level codingCorrelative level coding
Correlative level codingsrkrishna341
 
Speech signal time frequency representation
Speech signal time frequency representationSpeech signal time frequency representation
Speech signal time frequency representationNikolay Karpov
 
Signal & systems
Signal & systemsSignal & systems
Signal & systemsAJAL A J
 

Similar a Photodetection and photodetectors (20)

ECE375_Lec2_signal processing for engineering.pdf
ECE375_Lec2_signal processing for engineering.pdfECE375_Lec2_signal processing for engineering.pdf
ECE375_Lec2_signal processing for engineering.pdf
 
Unit 1 -Introduction to signals and standard signals
Unit 1 -Introduction to signals  and standard signalsUnit 1 -Introduction to signals  and standard signals
Unit 1 -Introduction to signals and standard signals
 
D1150740001
D1150740001D1150740001
D1150740001
 
Photo detector noise
Photo detector noisePhoto detector noise
Photo detector noise
 
Cognitive radio spectrum sensing and performance evaluation of energy detecto...
Cognitive radio spectrum sensing and performance evaluation of energy detecto...Cognitive radio spectrum sensing and performance evaluation of energy detecto...
Cognitive radio spectrum sensing and performance evaluation of energy detecto...
 
Cognitive radio spectrum sensing and performance evaluation of energy detecto...
Cognitive radio spectrum sensing and performance evaluation of energy detecto...Cognitive radio spectrum sensing and performance evaluation of energy detecto...
Cognitive radio spectrum sensing and performance evaluation of energy detecto...
 
ece477_7.ppt
ece477_7.pptece477_7.ppt
ece477_7.ppt
 
RF Module Design - [Chapter 3] Linearity
RF Module Design - [Chapter 3]  LinearityRF Module Design - [Chapter 3]  Linearity
RF Module Design - [Chapter 3] Linearity
 
Ee443 phase locked loop - presentation - schwappach and brandy
Ee443   phase locked loop - presentation - schwappach and brandyEe443   phase locked loop - presentation - schwappach and brandy
Ee443 phase locked loop - presentation - schwappach and brandy
 
00e isi
00e isi00e isi
00e isi
 
Solved problems
Solved problemsSolved problems
Solved problems
 
Chapter 4.ppt
Chapter 4.pptChapter 4.ppt
Chapter 4.ppt
 
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
 
Optical Fiber Communication Part 3 Optical Digital Receiver
Optical Fiber Communication Part 3 Optical Digital ReceiverOptical Fiber Communication Part 3 Optical Digital Receiver
Optical Fiber Communication Part 3 Optical Digital Receiver
 
PPT Chapter-1-V1.pptx__26715_1_1539251776000.pptx.pptx
PPT Chapter-1-V1.pptx__26715_1_1539251776000.pptx.pptxPPT Chapter-1-V1.pptx__26715_1_1539251776000.pptx.pptx
PPT Chapter-1-V1.pptx__26715_1_1539251776000.pptx.pptx
 
An automated and user-friendly optical tweezers for biomolecular investigat...
An automated and user-friendly optical  tweezers for biomolecular  investigat...An automated and user-friendly optical  tweezers for biomolecular  investigat...
An automated and user-friendly optical tweezers for biomolecular investigat...
 
Correlative level coding
Correlative level codingCorrelative level coding
Correlative level coding
 
non linear optics
non linear opticsnon linear optics
non linear optics
 
Speech signal time frequency representation
Speech signal time frequency representationSpeech signal time frequency representation
Speech signal time frequency representation
 
Signal & systems
Signal & systemsSignal & systems
Signal & systems
 

Más de wtyru1989

Quantum optical measurement
Quantum optical measurementQuantum optical measurement
Quantum optical measurementwtyru1989
 
Gaussian discord imperial
Gaussian discord imperialGaussian discord imperial
Gaussian discord imperialwtyru1989
 
Entropic characteristics of quantum channels and the additivity problem
Entropic characteristics of quantum channels and the additivity problemEntropic characteristics of quantum channels and the additivity problem
Entropic characteristics of quantum channels and the additivity problemwtyru1989
 
Manipulating continuous variable photonic entanglement
Manipulating continuous variable photonic entanglementManipulating continuous variable photonic entanglement
Manipulating continuous variable photonic entanglementwtyru1989
 
The gaussian minimum entropy conjecture
The gaussian minimum entropy conjectureThe gaussian minimum entropy conjecture
The gaussian minimum entropy conjecturewtyru1989
 
The security of quantum cryptography
The security of quantum cryptographyThe security of quantum cryptography
The security of quantum cryptographywtyru1989
 
Entanglement of formation
Entanglement of formationEntanglement of formation
Entanglement of formationwtyru1989
 
Bound entanglement is not rare
Bound entanglement is not rareBound entanglement is not rare
Bound entanglement is not rarewtyru1989
 
Continuous variable quantum entanglement and its applications
Continuous variable quantum entanglement and its applicationsContinuous variable quantum entanglement and its applications
Continuous variable quantum entanglement and its applicationswtyru1989
 
Relative entropy and_squahed_entanglement
Relative entropy and_squahed_entanglementRelative entropy and_squahed_entanglement
Relative entropy and_squahed_entanglementwtyru1989
 
Towards a one shot entanglement theory
Towards a one shot entanglement theoryTowards a one shot entanglement theory
Towards a one shot entanglement theorywtyru1989
 
Postselection technique for quantum channels and applications for qkd
Postselection technique for quantum channels and applications for qkdPostselection technique for quantum channels and applications for qkd
Postselection technique for quantum channels and applications for qkdwtyru1989
 
Encrypting with entanglement matthias christandl
Encrypting with entanglement matthias christandlEncrypting with entanglement matthias christandl
Encrypting with entanglement matthias christandlwtyru1989
 
Qkd and de finetti theorem
Qkd and de finetti theoremQkd and de finetti theorem
Qkd and de finetti theoremwtyru1989
 
Dic rd theory_quantization_07
Dic rd theory_quantization_07Dic rd theory_quantization_07
Dic rd theory_quantization_07wtyru1989
 
Lattices, sphere packings, spherical codes
Lattices, sphere packings, spherical codesLattices, sphere packings, spherical codes
Lattices, sphere packings, spherical codeswtyru1989
 
标量量化
标量量化标量量化
标量量化wtyru1989
 
Fully understanding cmrr taiwan-2012
Fully understanding cmrr taiwan-2012Fully understanding cmrr taiwan-2012
Fully understanding cmrr taiwan-2012wtyru1989
 
Op amp tutorial-1
Op amp tutorial-1Op amp tutorial-1
Op amp tutorial-1wtyru1989
 

Más de wtyru1989 (20)

Quantum optical measurement
Quantum optical measurementQuantum optical measurement
Quantum optical measurement
 
Gaussian discord imperial
Gaussian discord imperialGaussian discord imperial
Gaussian discord imperial
 
Entropic characteristics of quantum channels and the additivity problem
Entropic characteristics of quantum channels and the additivity problemEntropic characteristics of quantum channels and the additivity problem
Entropic characteristics of quantum channels and the additivity problem
 
Manipulating continuous variable photonic entanglement
Manipulating continuous variable photonic entanglementManipulating continuous variable photonic entanglement
Manipulating continuous variable photonic entanglement
 
The gaussian minimum entropy conjecture
The gaussian minimum entropy conjectureThe gaussian minimum entropy conjecture
The gaussian minimum entropy conjecture
 
The security of quantum cryptography
The security of quantum cryptographyThe security of quantum cryptography
The security of quantum cryptography
 
Entanglement of formation
Entanglement of formationEntanglement of formation
Entanglement of formation
 
Bound entanglement is not rare
Bound entanglement is not rareBound entanglement is not rare
Bound entanglement is not rare
 
Continuous variable quantum entanglement and its applications
Continuous variable quantum entanglement and its applicationsContinuous variable quantum entanglement and its applications
Continuous variable quantum entanglement and its applications
 
Relative entropy and_squahed_entanglement
Relative entropy and_squahed_entanglementRelative entropy and_squahed_entanglement
Relative entropy and_squahed_entanglement
 
Towards a one shot entanglement theory
Towards a one shot entanglement theoryTowards a one shot entanglement theory
Towards a one shot entanglement theory
 
Postselection technique for quantum channels and applications for qkd
Postselection technique for quantum channels and applications for qkdPostselection technique for quantum channels and applications for qkd
Postselection technique for quantum channels and applications for qkd
 
Encrypting with entanglement matthias christandl
Encrypting with entanglement matthias christandlEncrypting with entanglement matthias christandl
Encrypting with entanglement matthias christandl
 
Qkd and de finetti theorem
Qkd and de finetti theoremQkd and de finetti theorem
Qkd and de finetti theorem
 
Dic rd theory_quantization_07
Dic rd theory_quantization_07Dic rd theory_quantization_07
Dic rd theory_quantization_07
 
Lattices, sphere packings, spherical codes
Lattices, sphere packings, spherical codesLattices, sphere packings, spherical codes
Lattices, sphere packings, spherical codes
 
Em method
Em methodEm method
Em method
 
标量量化
标量量化标量量化
标量量化
 
Fully understanding cmrr taiwan-2012
Fully understanding cmrr taiwan-2012Fully understanding cmrr taiwan-2012
Fully understanding cmrr taiwan-2012
 
Op amp tutorial-1
Op amp tutorial-1Op amp tutorial-1
Op amp tutorial-1
 

Último

Ensuring Technical Readiness For Copilot in Microsoft 365
Ensuring Technical Readiness For Copilot in Microsoft 365Ensuring Technical Readiness For Copilot in Microsoft 365
Ensuring Technical Readiness For Copilot in Microsoft 3652toLead Limited
 
New from BookNet Canada for 2024: BNC CataList - Tech Forum 2024
New from BookNet Canada for 2024: BNC CataList - Tech Forum 2024New from BookNet Canada for 2024: BNC CataList - Tech Forum 2024
New from BookNet Canada for 2024: BNC CataList - Tech Forum 2024BookNet Canada
 
Dev Dives: Streamline document processing with UiPath Studio Web
Dev Dives: Streamline document processing with UiPath Studio WebDev Dives: Streamline document processing with UiPath Studio Web
Dev Dives: Streamline document processing with UiPath Studio WebUiPathCommunity
 
TrustArc Webinar - How to Build Consumer Trust Through Data Privacy
TrustArc Webinar - How to Build Consumer Trust Through Data PrivacyTrustArc Webinar - How to Build Consumer Trust Through Data Privacy
TrustArc Webinar - How to Build Consumer Trust Through Data PrivacyTrustArc
 
The Fit for Passkeys for Employee and Consumer Sign-ins: FIDO Paris Seminar.pptx
The Fit for Passkeys for Employee and Consumer Sign-ins: FIDO Paris Seminar.pptxThe Fit for Passkeys for Employee and Consumer Sign-ins: FIDO Paris Seminar.pptx
The Fit for Passkeys for Employee and Consumer Sign-ins: FIDO Paris Seminar.pptxLoriGlavin3
 
SALESFORCE EDUCATION CLOUD | FEXLE SERVICES
SALESFORCE EDUCATION CLOUD | FEXLE SERVICESSALESFORCE EDUCATION CLOUD | FEXLE SERVICES
SALESFORCE EDUCATION CLOUD | FEXLE SERVICESmohitsingh558521
 
How AI, OpenAI, and ChatGPT impact business and software.
How AI, OpenAI, and ChatGPT impact business and software.How AI, OpenAI, and ChatGPT impact business and software.
How AI, OpenAI, and ChatGPT impact business and software.Curtis Poe
 
What's New in Teams Calling, Meetings and Devices March 2024
What's New in Teams Calling, Meetings and Devices March 2024What's New in Teams Calling, Meetings and Devices March 2024
What's New in Teams Calling, Meetings and Devices March 2024Stephanie Beckett
 
Scanning the Internet for External Cloud Exposures via SSL Certs
Scanning the Internet for External Cloud Exposures via SSL CertsScanning the Internet for External Cloud Exposures via SSL Certs
Scanning the Internet for External Cloud Exposures via SSL CertsRizwan Syed
 
New from BookNet Canada for 2024: Loan Stars - Tech Forum 2024
New from BookNet Canada for 2024: Loan Stars - Tech Forum 2024New from BookNet Canada for 2024: Loan Stars - Tech Forum 2024
New from BookNet Canada for 2024: Loan Stars - Tech Forum 2024BookNet Canada
 
Unleash Your Potential - Namagunga Girls Coding Club
Unleash Your Potential - Namagunga Girls Coding ClubUnleash Your Potential - Namagunga Girls Coding Club
Unleash Your Potential - Namagunga Girls Coding ClubKalema Edgar
 
Tampa BSides - Chef's Tour of Microsoft Security Adoption Framework (SAF)
Tampa BSides - Chef's Tour of Microsoft Security Adoption Framework (SAF)Tampa BSides - Chef's Tour of Microsoft Security Adoption Framework (SAF)
Tampa BSides - Chef's Tour of Microsoft Security Adoption Framework (SAF)Mark Simos
 
"Subclassing and Composition – A Pythonic Tour of Trade-Offs", Hynek Schlawack
"Subclassing and Composition – A Pythonic Tour of Trade-Offs", Hynek Schlawack"Subclassing and Composition – A Pythonic Tour of Trade-Offs", Hynek Schlawack
"Subclassing and Composition – A Pythonic Tour of Trade-Offs", Hynek SchlawackFwdays
 
Commit 2024 - Secret Management made easy
Commit 2024 - Secret Management made easyCommit 2024 - Secret Management made easy
Commit 2024 - Secret Management made easyAlfredo García Lavilla
 
The Ultimate Guide to Choosing WordPress Pros and Cons
The Ultimate Guide to Choosing WordPress Pros and ConsThe Ultimate Guide to Choosing WordPress Pros and Cons
The Ultimate Guide to Choosing WordPress Pros and ConsPixlogix Infotech
 
Nell’iperspazio con Rocket: il Framework Web di Rust!
Nell’iperspazio con Rocket: il Framework Web di Rust!Nell’iperspazio con Rocket: il Framework Web di Rust!
Nell’iperspazio con Rocket: il Framework Web di Rust!Commit University
 
DevoxxFR 2024 Reproducible Builds with Apache Maven
DevoxxFR 2024 Reproducible Builds with Apache MavenDevoxxFR 2024 Reproducible Builds with Apache Maven
DevoxxFR 2024 Reproducible Builds with Apache MavenHervé Boutemy
 
What is DBT - The Ultimate Data Build Tool.pdf
What is DBT - The Ultimate Data Build Tool.pdfWhat is DBT - The Ultimate Data Build Tool.pdf
What is DBT - The Ultimate Data Build Tool.pdfMounikaPolabathina
 
How to write a Business Continuity Plan
How to write a Business Continuity PlanHow to write a Business Continuity Plan
How to write a Business Continuity PlanDatabarracks
 
Streamlining Python Development: A Guide to a Modern Project Setup
Streamlining Python Development: A Guide to a Modern Project SetupStreamlining Python Development: A Guide to a Modern Project Setup
Streamlining Python Development: A Guide to a Modern Project SetupFlorian Wilhelm
 

Último (20)

Ensuring Technical Readiness For Copilot in Microsoft 365
Ensuring Technical Readiness For Copilot in Microsoft 365Ensuring Technical Readiness For Copilot in Microsoft 365
Ensuring Technical Readiness For Copilot in Microsoft 365
 
New from BookNet Canada for 2024: BNC CataList - Tech Forum 2024
New from BookNet Canada for 2024: BNC CataList - Tech Forum 2024New from BookNet Canada for 2024: BNC CataList - Tech Forum 2024
New from BookNet Canada for 2024: BNC CataList - Tech Forum 2024
 
Dev Dives: Streamline document processing with UiPath Studio Web
Dev Dives: Streamline document processing with UiPath Studio WebDev Dives: Streamline document processing with UiPath Studio Web
Dev Dives: Streamline document processing with UiPath Studio Web
 
TrustArc Webinar - How to Build Consumer Trust Through Data Privacy
TrustArc Webinar - How to Build Consumer Trust Through Data PrivacyTrustArc Webinar - How to Build Consumer Trust Through Data Privacy
TrustArc Webinar - How to Build Consumer Trust Through Data Privacy
 
The Fit for Passkeys for Employee and Consumer Sign-ins: FIDO Paris Seminar.pptx
The Fit for Passkeys for Employee and Consumer Sign-ins: FIDO Paris Seminar.pptxThe Fit for Passkeys for Employee and Consumer Sign-ins: FIDO Paris Seminar.pptx
The Fit for Passkeys for Employee and Consumer Sign-ins: FIDO Paris Seminar.pptx
 
SALESFORCE EDUCATION CLOUD | FEXLE SERVICES
SALESFORCE EDUCATION CLOUD | FEXLE SERVICESSALESFORCE EDUCATION CLOUD | FEXLE SERVICES
SALESFORCE EDUCATION CLOUD | FEXLE SERVICES
 
How AI, OpenAI, and ChatGPT impact business and software.
How AI, OpenAI, and ChatGPT impact business and software.How AI, OpenAI, and ChatGPT impact business and software.
How AI, OpenAI, and ChatGPT impact business and software.
 
What's New in Teams Calling, Meetings and Devices March 2024
What's New in Teams Calling, Meetings and Devices March 2024What's New in Teams Calling, Meetings and Devices March 2024
What's New in Teams Calling, Meetings and Devices March 2024
 
Scanning the Internet for External Cloud Exposures via SSL Certs
Scanning the Internet for External Cloud Exposures via SSL CertsScanning the Internet for External Cloud Exposures via SSL Certs
Scanning the Internet for External Cloud Exposures via SSL Certs
 
New from BookNet Canada for 2024: Loan Stars - Tech Forum 2024
New from BookNet Canada for 2024: Loan Stars - Tech Forum 2024New from BookNet Canada for 2024: Loan Stars - Tech Forum 2024
New from BookNet Canada for 2024: Loan Stars - Tech Forum 2024
 
Unleash Your Potential - Namagunga Girls Coding Club
Unleash Your Potential - Namagunga Girls Coding ClubUnleash Your Potential - Namagunga Girls Coding Club
Unleash Your Potential - Namagunga Girls Coding Club
 
Tampa BSides - Chef's Tour of Microsoft Security Adoption Framework (SAF)
Tampa BSides - Chef's Tour of Microsoft Security Adoption Framework (SAF)Tampa BSides - Chef's Tour of Microsoft Security Adoption Framework (SAF)
Tampa BSides - Chef's Tour of Microsoft Security Adoption Framework (SAF)
 
"Subclassing and Composition – A Pythonic Tour of Trade-Offs", Hynek Schlawack
"Subclassing and Composition – A Pythonic Tour of Trade-Offs", Hynek Schlawack"Subclassing and Composition – A Pythonic Tour of Trade-Offs", Hynek Schlawack
"Subclassing and Composition – A Pythonic Tour of Trade-Offs", Hynek Schlawack
 
Commit 2024 - Secret Management made easy
Commit 2024 - Secret Management made easyCommit 2024 - Secret Management made easy
Commit 2024 - Secret Management made easy
 
The Ultimate Guide to Choosing WordPress Pros and Cons
The Ultimate Guide to Choosing WordPress Pros and ConsThe Ultimate Guide to Choosing WordPress Pros and Cons
The Ultimate Guide to Choosing WordPress Pros and Cons
 
Nell’iperspazio con Rocket: il Framework Web di Rust!
Nell’iperspazio con Rocket: il Framework Web di Rust!Nell’iperspazio con Rocket: il Framework Web di Rust!
Nell’iperspazio con Rocket: il Framework Web di Rust!
 
DevoxxFR 2024 Reproducible Builds with Apache Maven
DevoxxFR 2024 Reproducible Builds with Apache MavenDevoxxFR 2024 Reproducible Builds with Apache Maven
DevoxxFR 2024 Reproducible Builds with Apache Maven
 
What is DBT - The Ultimate Data Build Tool.pdf
What is DBT - The Ultimate Data Build Tool.pdfWhat is DBT - The Ultimate Data Build Tool.pdf
What is DBT - The Ultimate Data Build Tool.pdf
 
How to write a Business Continuity Plan
How to write a Business Continuity PlanHow to write a Business Continuity Plan
How to write a Business Continuity Plan
 
Streamlining Python Development: A Guide to a Modern Project Setup
Streamlining Python Development: A Guide to a Modern Project SetupStreamlining Python Development: A Guide to a Modern Project Setup
Streamlining Python Development: A Guide to a Modern Project Setup
 

Photodetection and photodetectors

  • 1. Lecture 2: Photodetection and Photodetectors ECE228B, Prof. D. J. Blumenthal Lecture 2, Slide 1
  • 2. Photodetection (Continued) ECE228B, Prof. D. J. Blumenthal Lecture 2, Slide 2
  • 3. Electrical Signal-to-Noise Ratio (SNR) ➥ At the receiver, there is noise on the signal arriving at the input and and after detection added to that is noise that is injected at various stages of the receiver ➥ The current output of the receiver in(t) has current contributions from ➥ Electrical shot noise ➥ Thermal noise ➥ APD detectors have additional multiplication noise ➥ Amplifier noise photodetector Popt(t) = PSig(t) + Pn(t) I(t) = Ip(t) + in(t) Receiver Detector Output Current (I) 2σ1 <I1> <I0> 2σ0 t ECE228B, Prof. D. J. Blumenthal Lecture 2, Slide 3
  • 4. Modeling Detector SNR  When observing the detector current output, it is difficult to tell which noise was present at the optical input and which noise was generated internal to the detector. So we tend to use several different models and combine them Optical signal = DC Current = DC Current = DC Current = DC component + component + component + component + variance (Poisson variance (Poisson variance (Filtered variance (Filtered Process) Process) Poisson Process) Poisson Process + Hd(ω) = FT {hd (t)} additive noise) ! Ideal photodetector Filtering Internal Detector Noise ECE228B, Prof. D. J. Blumenthal Lecture 2, Slide 4
  • 5. Noise Current  To quantify the statistical nature of noise, we can’t determine random events ahead of time, but we can use their “spectral” characteristics to quantify statistical behavior  Define an Average (mean) value to quantify the amount of power (energy) in the non- time varying part of the signal  Define a Variance to quantify the amount of power (energy) in the noisy part of the signal  Define the “noise” current as i(t) = I DC + inoise (t) ECE228B, Prof. D. J. Blumenthal Lecture 2, Slide 5
  • 6. Shot Noise Mean and Variance  For constant power illumination, the rate parameter is constant, and the signal is the mean  The noise corresponds to the photocurrent variance  For a filter, homogeneous Poisson process t !q Mean (Amps) is (t) = i(t) = Precvd $ hd (# )d# • Both mean and variance are h" 0 linear with Prevd t • As Prcvd is increased, both 2 !q 2 signal and noise increase Variance (Amps2) i (t) = var{i(t)} = n Precvd $ hd (# )d# h" 0 Power Spectrum 2 I DC Total Shot Noise = Area = 2qI DC B 2 in (0) = 2qI DC f B Detector Bandwidth ECE228B, Prof. D. J. Blumenthal Lecture 2, Slide 6
  • 7. Photodetector Shot Noise  The shot noise generated in the photodetection process is physically due to the “quantum granularity” of the received (and photo converted) optical signal  Shot noise sets the ultimate limit of an optical receiver  Shot noise is a Poisson noise, but it is usually approximated as a Gaussian noise  Hallmark of shot noise is dependence on q, the electron charge Constant Input Optical Power Detector Shot Noise Optically induced current + random electron fluctuations σ2Shot = 2q(Ip + Id)Δf Popt I Pin Detector <Ip> (BW = Δf) I(t) = <Ip> + ishot(t) 2σshot t t Poisson, no-modulation Thermally induced current + random electron fluctuations Dark Current (Id) Detector Shot Noise ECE228B, Prof. D. J. Blumenthal Lecture 2, Slide 7
  • 8. Shot Noise with Data Modulation  Consider how the picture changes when we have information modulated on the optical carrier  Let m(t) be the information transmitted  Then Prcvd (t) and λ(t) are functions of m(t)  Assuming the photodetector filter impulse function can change in amplitude from time period to time period, let Gj be a time varying parameter N i(t) = # G j hd (t ! " j ) j =1 Power Spectrum t ! is (t) = i(t) = G ' Prcvd (# )hd (t % # )d# $ 2 I DC Total Shot Noise = h" %& Modulation Area = 2qI DC B t 2 ! 2 2 2qI DC i (t) = var{i(t)} = n G & Prcvd (# )hd (t $ # )d# h" $% f Modulation Bandwidth B Detector Bandwidth ECE228B, Prof. D. J. Blumenthal Lecture 2, Slide 8
  • 9. Ideal Direct Detection (1) Ideal Amplifier = unity gain, zero noise, equivalent load RL Ei(t) i’(t) Hd(ω) = FT {hd(t)} AV=1 i(t) Vout(t) Ideal Detector RL # !q Ei (t) 2 & i(t) = LPF % ( Ei (t) = 2Ps Z 0 cos(! s t + " ) % h" Z 0 ( $ ' Vout (t) = i(t)RL # Ei (t) 2 & # !q 2Ps Z 0 & = LPF % Cos 2 () s t + * ) ( !q % ( $ h" Z 0 ' = idc RL = Ps RL Pavg =% 2 ( = Ps h" % Z0 ( # !q 1 & = LPF % 2Ps [1 + cos 2() s t + * )]( % $ ( ' $ h" 2 ' !q = idc = Ps h" ECE228B, Prof. D. J. Blumenthal Lecture 2, Slide 9
  • 10. Ideal Direct Detection (2)  Electrical SNR is found using the ratio between the signal power (DC) generated in the load resistor and the noise power (shot noise) generated in the load resistor 2 # !q & Psignal ( Psignal isignal RL % h" 2 $ ' 1 !Psignal SNRdd = = 2 = = Pnoise inoise RL 2qh" Psignal B 2 h" B  This equation shows the fundamental, quantum shot noise limit, where the SNR is limited only by the shot noise itself -> Shot Noise Limited Direct (Incoherent) Detection  SNR improves linearly with input signal strength  We will discuss other noise contributions that exist that make it difficult to reach this limit ECE228B, Prof. D. J. Blumenthal Lecture 2, Slide 10
  • 11. Ideal Coherent Detection (1)  Consider the following ideal Heterodyne Coherent Receiver  Heterodyne implies that a non-zero intermediate frequency (ωIF) is generated prior to data recovery Elo(t) Ideal Amplifier = unity gain, zero Local noise, equivalent load RL Oscillator ε Plo+ (1-ε) Psignal ε Es(t) i’(t) Hd(ω) = FT {hd(t)} AV=1 Ideal i(t) Vout(t) Input Power Detector Combiner RL ' !q 1 2* i(t) = LPF ) # 2Plo Z 0 cos($ lot + % ) + 1 & # 2Prcvd Z 0 cos($ s t + % ) , Ei (t) = ! Elo (t) + 1 " ! Es (t) ( h" Z 0 + 1 1 = ! 2Plo Z 0 cos(# lot + $ ) using cos - cos . = cos(- & . ) + cos(- + . ) 2 2 + 1 " ! 2Prcvd Z 0 cos(# s t + $ ) ' !q * i(t) = LPF ) ( h" { ( } Plo# + Prcvd (1 & # ) + 2 Plo Prcvd # (1 & # ) cos '($ s & $ lo ) t + % * , + + % Ei (t) 2 ( ' * Since typically Prcvd = Plo Pavg =' 2 * = Ps !q Z0 I DC ; Plo# ' * h" ' & * ) Assuming the intermmediate frequency ($ IF = $ s & $ lo ) falls within the LPF bandwidth !q i(t) = 2 Plo Prcvd # (1 & # ) cos [$ IF t + % ] h" ECE228B, Prof. D. J. Blumenthal Lecture 2, Slide 11
  • 12. Ideal Coherent Detection (2)  Using the same approach as in direct detection to obtain the SNR 2 ! i peak $ SNRhet = Psignal = 2 2 isignal RL = (irms )2 = # 2 & " % Pnoise inoise RL 2qI DC BRL 2qI DC BRL 2 ! 'q $ 2 Plo Prcvd ) (1 * ) ) # h( & # 2 & # " & % '(1 * ) )Prcvd 'Prcvd = = ; ! 'q $ h( B h( B limit Plo +,, ) +0 2q # Plo) & BRL " h( %  Note that shot noise limited heterodyne coherent detection, in the limit where the local oscillator is much stronger than the received signal,  Is a factor of 2 (3dB) better than the shot noise limited incoherent detection ECE228B, Prof. D. J. Blumenthal Lecture 2, Slide 12
  • 13. Ideal Coherent Detection (3)  The other coherent approach is Homodyne Coherent Detection  The intermediate frequency (ωIF) is driven to zero (ωIF=0) at phase is driven to φ=0 bringing the data immediately to baseband Automatic Local Ideal Amplifier = unity gain, zero Frequency/Phase Oscillator noise, equivalent load RL Elo(t) Control ε Plo+ (1-ε) Psignal ε Es(t) i’(t) Hd(ω) = FT {hd(t)} AV=1 Ideal i(t) Vout(t) Input Power Detector Combiner RL ' !q 1 2* Ei (t) = ! Elo (t) + 1 " ! Es (t) i(t) = LPF ) # 2Plo Z 0 cos($ lot + % ) + 1 & # 2Prcvd Z 0 cos($ s t + % ) , ( h" Z 0 + = ! 2Plo Z 0 cos(# lot + $ ) + 1 " ! 2Prcvd Z 0 cos(# s t + $ ) Since we are using an AFC/APC control to drive $ IF = 0 ' !q * % Ei (t) 2 ( i(t) = LPF ) ( h" { } Plo# + Prcvd (1 & # ) + 2 Plo Prcvd # (1 & # ) , + ' * Pavg =' 2 * = Ps Since typically Prcvd = Plo Z0 ' * !q ' * I DC ; Plo# & ) h" !q i(t) = 2 Plo Prcvd # (1 & # ) ECE228B, Prof. D. J. Blumenthal h" Lecture 2, Slide 13
  • 14. Ideal Coherent Detection (4)  Using the same approach as in direct detection to obtain the SNR SNRhet = Psignal = 2 isignal RL = (irms )2 2 Pnoise inoise RL 2qI DC B 2 % !q ( ' 2 Plo Prcvd # (1 $ # ) * & h" ) ! 2(1 $ # )Prcvd !P = = ; 2 rcvd % !q ( h" B h" B limit Plo +,, # +0 2q ' Plo# * B & h" )  Note that shot noise limited homodyne coherent detection, in the limit where the local oscillator is much stronger than the received signal,  Is a factor of 2 (3dB) better than the shot noise limited heterodyne receiver and factor of 4 (6dB) better than the shot noise limited incoherent detection ECE228B, Prof. D. J. Blumenthal Lecture 2, Slide 14
  • 15. Photodetectors ECE228B, Prof. D. J. Blumenthal Lecture 2, Slide 15
  • 16. Photoconductors (1) ➱ Photon absorption in semiconductor materials. ➱ Three main absorption mechanisms: Intrinsic (band-to-band), Free-Carrier Absorption and Band-and-Impurity Absorption ➱ Intrinsic (band-to-band) is the dominant effect in most SC photoconductors Intrinsic (band-to-band) Free-Carrier Absorption Band-and-Impurity Absorption e- e- Ephoton = hν Ec h+ Ec e- Ec Ephoton = hν Ephoton = hν + Donor Level - Acceptor Level Ephoton = hν h+ Ev Ev Ev h+ •Incident photon Ephoton= hν= Ec - Ev ECE228B, Prof. D. J. Blumenthal Lecture 2, Slide 16
  • 17. Photoconductors (2) ➱ For intrinsic absorption, photons can be absorbed if hc 1.24 ! ( µ m) > = Ec " EV Eg (eV ) 1240 ! (nm) > Eg (eV ) Material Bandgap (eV) Maximum λ (nm) Typical Operating Range (nm) Si 1.12 1110 500-900 Ge 0.67 1850 900-1300 GaAs 1.43 870 750-850 InxGa1-xAsyP1-y 0.38-2.25 550-3260 1000-1600 ECE228B, Prof. D. J. Blumenthal Lecture 2, Slide 17
  • 18. Photoconductors (3) Ephoton = hν Semiconductor ➱ Define: ➱ Pi = incident optical power ➱ R(λ) power reflectivity from input Pi medium to semiconductor Pi(1-R) Pi(1-R)e-αx ➱ α(λ) = 1/e absorption length ➱ 1/ α(λ) = penetration depth x 1/α ➱ Power absorbed by the semiconductor is Pabs (x) = Pi (1 ! R)(1 ! e!" ( # )x ) = $(#, x)Pi ➱ defining the efficiency number of photocarriers produced !(", x) = number of incident photons = (1 # R)(1 # e#$ ( " )x ) 0 % !(", x) % 1 ECE228B, Prof. D. J. Blumenthal Lecture 2, Slide 18
  • 19. Photoconductive Photodetectors (1)  Photogenerated current will have time and wavelength dependence !q i photo (t) = GPrcvd (t) + idark h" Pi # carrier = mean free carrier lifetime # transit = transit time between eletrical contacts $# ' G = & carrier ) = photoconductive gain % # transit ( idark = dark current Metal Metal Semiconductor  The transit time for electrons and holes can be different and in many SCs the eletron mobility is greater than that of the hole ! e = µe E > µh E = ! h + iphoto  The SC must remain charge neutral, for every electron generated, multiple holes will get pulled in until the photogenerated electron reaches the other contact. The carrier and transit times are limited by the slower carrier and the photoconductive Vbias gain is given by the ratio of the transit times L ! carrier = a "h L ! transit = a "e ECE228B, Prof. D. J. Blumenthal Lecture 2, Slide 19
  • 20. Photoconductive Photodetectors (2)  The carrier velocity is a linear function of electric field strength up to a saturation velocity (which is the same for both electrons and holes)  Field strength of about 105 V/cm result in velocities in range of 6x106 to 107 cm/s  Some materials have an electron drift velocity that peaks at 2x107 cm/s at 104 V/cm  When photoconductive gain is desirable, detector is operated at low voltages  Carrier lifetime also impacts the frequency response of the photoconductive photodetector Prcvd (! ) i photo (! ) = "G 2 #!& 1+ % ( $ !c ' 1 !c = = cutoff frequency ) carrier ECE228B, Prof. D. J. Blumenthal Lecture 2, Slide 20