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Sergey Vinogradov QUASAR group Department of Physics, University of Liverpool, UK

Beam Loss Monitoring – Detectors Photon Detection and Silicon Photomultiplier Technology in accelerator and particle physics. Sergey Vinogradov QUASAR group Department of Physics, University of Liverpool, UK Cockcroft Institute of Accelerator Science and Technology, UK

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Sergey Vinogradov QUASAR group Department of Physics, University of Liverpool, UK

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  1. Beam Loss Monitoring – DetectorsPhoton Detection and Silicon Photomultiplier Technology in accelerator and particle physics Sergey Vinogradov QUASAR group Department of Physics, University of Liverpool, UK Cockcroft Institute of Accelerator Science and Technology, UK P.N. Lebedev Physical Institute of the Russian Academy of Sciences, Russia Sergey Vinogradov oPAC Advanced School on Accelerator Optimization Royal Holloway University, London, UK, July 9th, 2014

  2. Content • 1. Introduction: the best photodetectors • 2. Silicon Photomultipliers (SiPM) as new photon number resolving detectors • 3. Benefits, drawbacks, and typical applications of SiPM • 4. Evaluation studies of SiPMs for Beam Loss Monitoring • 5. Modelling and analysis of comparative performance: SiPM vs PMT and APD • 6. Trends and prospects of SiPM technology for BLM and accelerator applications Sergey Vinogradov oPAC Advanced School on Accelerator Optimization Royal Holloway University, London, UK, July 9th, 2014 2

  3. Introduction • 1. Introduction: the best photodetectors • your choice? • 2. Silicon Photomultipliers (SiPM) as new photon number resolving detectors • 3. Benefits, drawbacks, and typical applications of SiPM • 4. Evaluation studies of SiPMs for Beam Loss Monitoring • 5. Modelling and analysis of comparative performance: SiPM vs PMT and APD • 6. Trends and prospects of SiPM technology for BLM and accelerator applications Sergey Vinogradov oPAC Advanced School on Accelerator Optimization Royal Holloway University, London, UK, July 9th, 2014 3

  4. Photodetector #1 * Adaptive focusing & trichromatic / monochromatic vision High sensitivity due to 100 million rod cells (10-40 photons) High resolution & double dynamic range due to 5 million cone cells High readout rate of 30 frame/s Internal signal processing (100M cells to 1M nerves @30fps) 540 million years old design (*) Yu. Musienko, NDIP 2011 Sergey Vinogradov oPAC Advanced School on Accelerator Optimization Royal Holloway University, London, UK, July 9th, 2014 4

  5. CCD/CMOS approach – toward to #1 Trichromatic / monochromatic vision Number of pixels up to ~ 50 M Sensitivity from ~ 10 -100 photons Dynamic range up to ~ 50K Readout up to ~ 1000 frame/s 40 years old design Sergey Vinogradov oPAC Advanced School on Accelerator Optimization Royal Holloway University, London, UK, July 9th, 2014 5

  6. SiPM approach – toward to ideal low photon detection 20th Anniversary ~ now! Sergey Vinogradov oPAC Advanced School on Accelerator Optimization Royal Holloway University, London, UK, July 9th, 2014 6

  7. Silicon Photomultipliers (SiPM) as new photon number resolving detectors • 1. Introduction: the best photodetectors • 2. Silicon Photomultipliers (SiPM) as new photon number resolving detectors • 3. Benefits, drawbacks, and typical applications of SiPM • 4. Evaluation studies of SiPMs for Beam Loss Monitoring • 5. Modelling and analysis of comparative performance: SiPM vs PMT and APD • 6. Trends and prospects of SiPM technology for BLM and accelerator applications Sergey Vinogradov oPAC Advanced School on Accelerator Optimization Royal Holloway University, London, UK, July 9th, 2014 7

  8. Concept of ideal detector:first step to SiPM • Ideal detector: conversion of any input signal starting from single photon to recognizable output without noise and distortion in amplitude and timing of the signal Real photon detector Ideal photon detector W. Farr, SPIE LEOS 2009 Sergey Vinogradov oPAC Advanced School on Accelerator Optimization Royal Holloway University, London, UK, July 9th, 2014

  9. Single photon detectionin 1 GHz BW with electronic noise 104 e Gain ~ 100 ENF ~ 3…10 Gain =1 ENF ~ 1 σnoise Gain ~ 1 M ENF ~ 1.2 Gain ~ 1 M ENF ~ 1.01 σ(Nout) Sergey Vinogradov oPAC Advanced School on Accelerator Optimization Royal Holloway University, London, UK, July 9th, 2014

  10. Avalanche with negative feedback: main step to SiPM Strong negative feedback = fast quenching & small charge fluctuations Higher Field V. Shubin, D. Shushakov, Avalanche Photodetectors, 2003 Sergey Vinogradov oPAC Advanced School on Accelerator Optimization Royal Holloway University, London, UK, July 9th, 2014

  11. Multi-pixel design & feedback resistor:final step to SiPM MRS APD – 1990s SiPM – 1996 / 2000s Fig. 1-4: Sadygov, NDIP 2005 Fig. 5-7: B. Dolgoshein et al., 2001-2005 Sergey Vinogradov oPAC Advanced School on Accelerator Optimization Royal Holloway University, London, UK, July 9th, 2014

  12. SiPM: photon number resolution APD (self-differencing mode) SiPM (MEPhI/Pulsar) VLPC B. Kardinal et al., Nat. Photonics, 2008 R. Mirzoyan et al., NDIP, 2008 PMT (Hamamatsu R5600) MPPC (Hamamatsu) SiPM (Excelitas) A. Barlow and J. Schilz, SiPM matching event, CERN, 2011 S. Vinogradov, SPIE 2011 I. Chirikov-Zorin et al, NIMA 2001 Sergey Vinogradov oPAC Advanced School on Accelerator Optimization Royal Holloway University, London, UK, July 9th, 2014

  13. Sergey Vinogradov Seminars on SiPM at the Cockcroft Institute 2 December 2013 13 G. Collazuol, PhotoDet, 2012

  14. Benefits, drawbacks, and typical applications of SiPM • 1. Introduction: the best photodetectors • 2. Silicon Photomultipliers (SiPM) as new photon number resolving detectors • 3. Benefits, drawbacks, and typical applications of SiPM • 4. Evaluation studies of SiPMs for Beam Loss Monitoring • 5. Modelling and analysis of comparative performance: SiPM vs PMT and APD • 6. Trends and prospects of SiPM technology for BLM and accelerator applications Sergey Vinogradov oPAC Advanced School on Accelerator Optimization Royal Holloway University, London, UK, July 9th, 2014 14

  15. SiPM: photon number resolution • SiPM looks like ~ ideal detector • Near-ideal amplification: • Gain > 105, ENF < 1.01 • Room temperature • Low bias (<100 V) • Large area (6x6 mm2) • Good timing (jitter < 200 ps) • Fast response (rise <1 ns, fall~20 ns) • In fact, not a photon spectrum • Photoelectrons • Dark electrons • Crosstalk & Afterpulses • In fact, non-Poissonian distribution • Why? • How much? • Distribution? • Resolution? SiPM (Excelitas) A. Barlow and J. Schilz, SiPM matching event, CERN, 2011 P. Finocchiaro et al., IEEE TNS, 2009 Sergey Vinogradov oPAC Advanced School on Accelerator Optimization Royal Holloway University, London, UK, July 9th, 2014

  16. SiPM drawbacks: crosstalk • Crosstalk: hot carrier photon emission + detection = false event A. Lacaita et al., IEEE TED, 1993 R. Mirzoyan, NDIP, 2008 Yu. Musienko, NDIP, 2005 Sergey Vinogradov oPAC Advanced School on Accelerator Optimization Royal Holloway University, London, UK, July 9th, 2014

  17. SiPM drawbacks: afterpulsing Output primary avalanche Δ time afterpulses • Afterpulsing: trapping + detrapping + detection = false event C. Piemonte et al., Perugia,2007 G. Collazuol, PhotoDet, 2012 Sergey Vinogradov oPAC Advanced School on Accelerator Optimization Royal Holloway University, London, UK, July 9th, 2014

  18. SiPM drawbacks: nonlinearity • Limited number of pixels = losses of photons • Dead time of pixels during recovery = losses of photons Ideal 100 pixel SSPM Ideal photon detector Plot details: Npixel=100 PDE=100% No Noise (DCR, CT, AP) Sergey Vinogradov oPAC Advanced School on Accelerator Optimization Royal Holloway University, London, UK, July 9th, 2014

  19. Application types • Binary detection of light pulses – “events” (bit error rate) - NA • Photon number resolution (noise-to-signal ratio, σn/μn) - Calorimetry • Time-of-flight detection (transit time spread, σt) – TOF PET • Detection of arbitrary signals starting from photon counting - Iph(t) - Beam Loss Monitoring Sergey Vinogradov oPAC Advanced School on Accelerator Optimization Royal Holloway University, London, UK, July 9th, 2014

  20. SiPM application examples • Calorimetry • SiPM (MEPhI) small HCAL (MINICAL), DESY, 2003 • MPPC (Hamamatsu), T2K, 2005-2009 • MPPC at LHC CMS HCAL • RICH for ALICE (LHC) • FermiLab, Jefferson Lab calorimeter upgrade projects • Astrophysics • SiPMcosmic ray detection in space (MEPhI, 2005) • Cherenkov light detection of air showers (CTA, 2013) • Medical imaging • Positron Emission Tomography: • TOF-PET • PET / MRI • Telecommunication • Quantum cryptography • Deep space laser link (Mars exploring program) Sergey Vinogradov oPAC Advanced School on Accelerator Optimization Royal Holloway University, London, UK, July 9th, 2014

  21. Evaluation studies of SiPMs for Beam Loss Monitoring • 1. Introduction: the best photodetectors • 2. Silicon Photomultipliers (SiPM) as new photon number resolving detectors • 3. Benefits, drawbacks, and typical applications of SiPM • 4. Evaluation studies of SiPMs for Beam Loss Monitoring • 5. Modelling and analysis of comparative performance: SiPM vs PMT and APD • 6. Trends and prospects of SiPM technology for BLM and accelerator applications Sergey Vinogradov oPAC Advanced School on Accelerator Optimization Royal Holloway University, London, UK, July 9th, 2014 21

  22. Beam Loss Monitoring (ref. E. Nebot talk 08-07-14) Objectives: Protect Monitor Adjust Sergey Vinogradov oPAC Advanced School on Accelerator Optimization Royal Holloway University, London, UK, July 9th, 2014

  23. BLM: first evaluation of SiPM D. Di Giovenale et al., NIMA, 2011 SPARC accelerator, Frascati, INFN FERMI@Elettra, Synchrotrone Trieste • MPPC, 1mm2, 400 pixels • Quartz fiber 300 μm, 100 m • Dark count noise: negligible • Electronic noise: negligible • Spectral dispersion in fiber: n(𝜆) →∆t(𝜆) ~ 3 ns @100 m • τfall ~ 10 ns → deconvolution • Compact low cost BLM • 1m-scale resolution @100 m Sergey Vinogradov oPAC Advanced School on Accelerator Optimization Royal Holloway University, London, UK, July 9th, 2014

  24. SiPM performance metrics for BLM • Loss scenarios reconstruction • Amplitude → # photons → # particles per location (PNR) • Transit time to rising edge → single loss location (Time Res.) • Resolution of multiple loss locations & # particles • Modulation transfer function (MTF) ? • Nonlinearity has to be accounted ! TTS PNR New metrics ? Sergey Vinogradov oPAC Advanced School on Accelerator Optimization Royal Holloway University, London, UK, July 9th, 2014

  25. Challenges for SiPM in BLM:saturation, recovering, duplications • Transient nonlinearity of SiPM response Large rectangular light pulse: Nph > Npix;Tpulse > Trec • Peak – initial avalanche events in ready-to-triggering pixels • Plateau – repetitive recovering and re-triggering of pixels • Fall – final recovering (without photons, but with afterpulses!) 4 us pulse 50 ns pulse Sergey Vinogradov oPAC Advanced School on Accelerator Optimization Royal Holloway University, London, UK, July 9th, 2014

  26. MPPC response on rectangular pulse Sergey Vinogradov oPAC Advanced School on Accelerator Optimization Royal Holloway University, London, UK, July 9th, 2014

  27. Modelling and analysis of comparative performance: SiPM vs PMT and APD • 1. Introduction: the best photodetectors • 2. Silicon Photomultipliers (SiPM) as new photon number resolving detectors • 3. Benefits, drawbacks, and typical applications of SiPM • 4. Evaluation studies of SiPMs for Beam Loss Monitoring • 5. Modelling and analysis of comparative performance: SiPM vs PMT and APD • 6. Trends and prospects of SiPM technology for BLM and accelerator applications Sergey Vinogradov oPAC Advanced School on Accelerator Optimization Royal Holloway University, London, UK, July 9th, 2014 27

  28. Photon Number Resolution • Photon Number Resolution & Excess Noise Factor • Burgess variance theorem Ideal 100 pixel SSPM Ideal photon detector Sergey Vinogradov oPAC Advanced School on Accelerator Optimization Royal Holloway University, London, UK, July 9th, 2014

  29. Schematics of AP & CT stochastic processes Duplication Models Single primary event N≡1 e.g. SSPM Dark Spectrum Poisson number of primaries <N>=μ e.g. SSPM Photon Spectrum Geometric Chain Afterpulsing Process Non-random (Dark) event Random primary (Photo) events Random Photo events Random CT events … Random CT events … Random CT events Primary 1st CT 2nd CT No CT Branching Poisson Crosstalk Process Non-random (Dark) event Random primary (Photo) events Random CT events … Random CT events … … … … … … … … … Sergey Vinogradov oPAC Advanced School on Accelerator Optimization Royal Holloway University, London, UK, July 9th, 2014

  30. Analytical results for CT & AP statistics CT & AP model results [1] S. Vinogradov et al., NSS/MIC 2009 λ is a mean number of successors in one branch generation Sergey Vinogradov oPAC Advanced School on Accelerator Optimization Royal Holloway University, London, UK, July 9th, 2014

  31. Crosstalk models and experiments A.N. Otte, JINST 2007 P. Finocchiaro et al., IEEE TNS, 2009 Pct=40% Pct=10% Dark event complimentary cumulative distribution – DCR vs. threshold (S. Vinogradov, NDIP 2011; experiment B. Dolgoshein et al., NDIP 2008) A few photon detection spectrum of Hamamatsu MPPC (S. Vinogradov, SPIE 2012) Sergey Vinogradov oPAC Advanced School on Accelerator Optimization Royal Holloway University, London, UK, July 9th, 2014

  32. SiPM binomial nonlinearity Output signal, Ns <Ns>=f(<Nph>) B. Dolgoshein et al., 2002 Photons per pulse, Nph E.B. Johnson, NSS/MIC 2008 Intrinsic Resolution in σ units; Npix=506 Sergey Vinogradov oPAC Advanced School on Accelerator Optimization Royal Holloway University, London, UK, July 9th, 2014

  33. SiPM recovery nonlinearity • Nonparalizible dead time model • Probability distribution (~ Gaussian) • W. Feller, An Introduction to Probability Theory and Its Applications, Vol. 2, Ch. XI, John Willey & Sons, Inc., 1968 Recovery non-linearity → ENF S. Vinogradov et al., IEEE NSS/MIC 2009 M.Grodzicka, NSS 2011 • Exponential recovery of Gain m(t) • accounting for Pdet(m) • S. Vinogradov, SPIE DSS, 2012 Plot details: PDE=100% Npixel=500 Tpulse=100 ns Sergey Vinogradov oPAC Advanced School on Accelerator Optimization Royal Holloway University, London, UK, July 9th, 2014

  34. Performance metrics: ENF and DQE Sergey Vinogradov oPAC Advanced School on Accelerator Optimization Royal Holloway University, London, UK, July 9th, 2014

  35. Performance in DQE – various detectors Gain Fm Gain PDE Sergey Vinogradov oPAC Advanced School on Accelerator Optimization Royal Holloway University, London, UK, July 9th, 2014

  36. Time resolution • Time resolution is combined as a sum of contributions • Transit time spread of photon arrival, avalanche triggering, avalanche development, and single electron response times • Jitter of signal amplitude fluctuations in a time scale Sergey Vinogradov oPAC Advanced School on Accelerator Optimization Royal Holloway University, London, UK, July 9th, 2014

  37. Filtered point process approach to amplitude fluctuations & time resolution Sergey Vinogradov oPAC Advanced School on Accelerator Optimization Royal Holloway University, London, UK, July 9th, 2014

  38. Clastered filtered point process model • Time resolution includes all essential factors and combines performance in time response and PNR (ENF) Sergey Vinogradov oPAC Advanced School on Accelerator Optimization Royal Holloway University, London, UK, July 9th, 2014

  39. Time resolution:scintillation model & experiment • Most popular & demanded case study: LYSO+MPPC • LYSO: 0.09 ns rise, 44 ns decay; 9% resolution MPPC: Npe=3900, ENFgain=1.015, Pct=0.14; SPTR=0.124 ns, Vnoise=0.32 mV S. Seifert et al, “A Comprehensive Mode to Predict the Timing Resolution ”, TNS, 2012. • MPPC SER pulse shape – analytical expression (~ 1 ns rise, ~ 25 ns decay) D. Marano et al, “Silicon Photomultipliers Electrical Model: Extensive Analytical Analysis” TNS 2014 Sergey Vinogradov oPAC Advanced School on Accelerator Optimization Royal Holloway University, London, UK, July 9th, 2014

  40. Arbitrary signal detection:rectangular pulse response model Sergey Vinogradov oPAC Advanced School on Accelerator Optimization Royal Holloway University, London, UK, July 9th, 2014 40

  41. Trends and prospects of SiPMs for BLM and accelerator applications • 1. Introduction: the best photodetectors • 2. Silicon Photomultipliers (SiPM) as new photon number resolving detectors • 3. Benefits, drawbacks, and typical applications of SiPM • 4. Evaluation studies of SiPMs for Beam Loss Monitoring • 5. Modelling and analysis of comparative performance: SiPM vs PMT and APD • 6. Trends and prospects of SiPM technology for BLM and accelerator applications Sergey Vinogradov oPAC Advanced School on Accelerator Optimization Royal Holloway University, London, UK, July 9th, 2014 41

  42. SiPM trends and advances • Market leaders • Hamamatsu • KETEK • SensL • FBK / AdvanSiD • Excelitas / Perkin Elmer • Philips (digital SiPM) • Design improvements (~ in a few year time scale) • Higher Photon Detection Efficiency (30% → 70%) • Lower crosstalk, lower afterpulsing (30% → 3%) • Lower dark count rate (1000 → 40 Kcps/mm2) • Faster SER, smaller pixel size (25 → 10 um) • Larger area, larger arrays (3x3→10x10mm2, 4x4 → 16x16 channels) Latest news from 2ndSiPM Advanced Workshopand Conf . on New Development s in Photodetection, 2014 Sergey Vinogradov oPAC Advanced School on Accelerator Optimization Royal Holloway University, London, UK, July 9th, 2014

  43. Hamamatsu: Through Silicon Vias Sergey Vinogradov oPAC Advanced School on Accelerator Optimization Royal Holloway University, London, UK, July 9th, 2014

  44. Hamamatsu: Low Crosstalk & Afterpulsing Sergey Vinogradov oPAC Advanced School on Accelerator Optimization Royal Holloway University, London, UK, July 9th, 2014

  45. Performance in DQE - MPPC series Pulse duration & detection time = 10 ns Sergey Vinogradov oPAC Advanced School on Accelerator Optimization Royal Holloway University, London, UK, July 9th, 2014

  46. KETEK • Highest PDE @50 um pixels • Various geometries • 15 … 100 um pixels Sergey Vinogradov oPAC Advanced School on Accelerator Optimization Royal Holloway University, London, UK, July 9th, 2014

  47. SensL • Fast capacitive output • FWHM < 3.2 ns @ 6x6 mm2 • Large arrays / modules • Low cost Sergey Vinogradov oPAC Advanced School on Accelerator Optimization Royal Holloway University, London, UK, July 9th, 2014

  48. Philips: Digital SiPM (Modern active quenching SPAD array) Sergey Vinogradov oPAC Advanced School on Accelerator Optimization Royal Holloway University, London, UK, July 9th, 2014

  49. Summary on BLM with SiPM • BLM is one of the most challenging application for SiPM • Benefits • Practical & efficient (cost, compactness, Si reliability…) • Perfect Transit Time Resolution (as is for now) • Acceptable DQE within dynamic range (may be better) • Drawbacks • Upper margin of dynamic range is low (design improvement) • Number / density of pixels (↑ 10 times) • Pixel recovery time (↓ 10 times) • Time response (bandwidth) (external measures) • Analog / digital SiPM output signal processing • BLM with SiPM: big problem with a chance to win • And with a lot of space for new ideas, designs, and fun Sergey Vinogradov Seminars on SiPM at the Cockcroft Institute 3 February 2014 49

  50. Summary on SiPM SiPM technology: breakthrough in photon detection Photon number resolution at room temperature Silicon technology / mass production / reliability / price Highly competitive in short (< μs) pulse detection Fast progress in improvements: DQE, Dynamic range, Timing Welcome to SiPM applications Scintillation Cherenkov Laser pulse And much more… Sergey Vinogradov oPAC Advanced School on Accelerator Optimization Royal Holloway University, London, UK, July 9th, 2014

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