Beam Loss Monitoring Detectors Photon Detection and Silicon

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 P. N. Lebedev Physical Institute of the Russian Academy of Sciences, Russia Sergey Vinogradov o. PAC Advanced School on Accelerator Optimization Royal Holloway University, London, UK, July 9 th, 2014

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

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

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 (100 M cells to 1 M nerves @30 fps) 540 million years old design (*) Yu. Musienko, NDIP 2011 Sergey Vinogradov o. PAC Advanced School on Accelerator Optimization Royal Holloway University, London, UK, July 9 th, 2014 4

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 ~ 50 K Readout up to ~ 1000 frame/s 40 years old design Sergey Vinogradov o. PAC Advanced School on Accelerator Optimization Royal Holloway University, London, UK, July 9 th, 2014 5

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

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

Concept of ideal detector: first step to Si. PM ◙ 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 o. PAC Advanced School on Accelerator Optimization Royal Holloway University, London, UK, July 9 th, 2014

Single photon detection in 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 o. PAC Advanced School on Accelerator Optimization Royal Holloway University, London, UK, July 9 th, 2014

Avalanche with negative feedback: main step to Si. PM Strong negative feedback = fast quenching & small charge fluctuations H Fi igh el er d V. Shubin, D. Shushakov, Avalanche Photodetectors, 2003 Sergey Vinogradov o. PAC Advanced School on Accelerator Optimization Royal Holloway University, London, UK, July 9 th, 2014

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

Si. PM: photon number resolution APD (self-differencing mode) VLPC Si. PM (MEPh. I/Pulsar) B. Kardinal et al. , Nat. Photonics, 2008 R. Mirzoyan et al. , NDIP, 2008 PMT (Hamamatsu R 5600) I. Chirikov-Zorin et al, NIMA 2001 Sergey Vinogradov MPPC (Hamamatsu) S. Vinogradov, SPIE 2011 o. PAC Advanced School on Accelerator Optimization Si. PM (Excelitas) A. Barlow and J. Schilz, Si. PM matching event, CERN, 2011 Royal Holloway University, London, UK, July 9 th, 2014

Vinogradov G. Sergey Collazuol, Photo. Det, 2012 Seminars on Si. PM at the Cockcroft Institute 2 December 2013 13

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

Si. PM: photon number resolution ◙ Si. PM looks like ~ ideal detector Near-ideal amplification: ― Gain > 105, ENF < 1. 01 Room temperature Low bias (<100 V) Large area (6 x 6 mm 2) Good timing (jitter < 200 ps) Fast response (rise <1 ns, fall~20 ns) Si. PM (Excelitas) A. Barlow and J. Schilz, Si. PM matching event, CERN, 2011 ◙ In fact, not a photon spectrum Photoelectrons Dark electrons Crosstalk & Afterpulses ◙ In fact, non-Poissonian distribution Why? How much? Distribution? Resolution? Sergey Vinogradov o. PAC Advanced School on Accelerator Optimization P. Finocchiaro et al. , IEEE TNS, 2009 Royal Holloway University, London, UK, July 9 th, 2014

Si. PM 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 o. PAC Advanced School on Accelerator Optimization Royal Holloway University, London, UK, July 9 th, 2014

Si. PM drawbacks: afterpulsing ◙ Afterpulsing: trapping + detection = false event Output primary avalanche Δ time afterpulses G. Collazuol, Photo. Det, 2012 Sergey Vinogradov C. Piemonte et al. , Perugia, 2007 o. PAC Advanced School on Accelerator Optimization Royal Holloway University, London, UK, July 9 th, 2014

Si. PM drawbacks: nonlinearity l 10 el 0 pix Idea SSP M Ide al ph oto nd ete cto r ◙ Limited number of pixels = losses of photons ◙ Dead time of pixels during recovery = losses of photons Plot details: Npixel=100 PDE=100% No Noise (DCR, CT, AP) Sergey Vinogradov o. PAC Advanced School on Accelerator Optimization Royal Holloway University, London, UK, July 9 th, 2014

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 o. PAC Advanced School on Accelerator Optimization Royal Holloway University, London, UK, July 9 th, 2014

Si. PM application examples ◙ Calorimetry Si. PM (MEPh. I) small HCAL (MINICAL), DESY, 2003 MPPC (Hamamatsu), T 2 K, 2005 -2009 MPPC at LHC CMS HCAL RICH for ALICE (LHC) Fermi. Lab, Jefferson Lab calorimeter upgrade projects ◙ Astrophysics Si. PM cosmic ray detection in space (MEPh. I, 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 o. PAC Advanced School on Accelerator Optimization Royal Holloway University, London, UK, July 9 th, 2014

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

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

BLM: first evaluation of Si. PM D. Di Giovenale et al. , NIMA, 2011 SPARC accelerator, Frascati, INFN FERMI@Elettra, Synchrotrone Trieste MPPC, 1 mm 2, 400 pixels Quartz fiber 300 μm, 100 m Dark count noise: negligible Electronic noise: negligible Spectral dispersion in fiber: n(�� ) →∆t(�� ) ~ 3 ns @100 m τfall ~ 10 ns → deconvolution ◙Compact low cost BLM 1 m-scale resolution @100 m Sergey Vinogradov o. PAC Advanced School on Accelerator Optimization Royal Holloway University, London, UK, July 9 th, 2014

Si. PM 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 New metrics ? Sergey Vinogradov o. PAC Advanced School on Accelerator Optimization PNR Royal Holloway University, London, UK, July 9 th, 2014

Challenges for Si. PM in BLM: saturation, recovering, duplications ◙ Transient nonlinearity of Si. PM 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 o. PAC Advanced School on Accelerator Optimization Royal Holloway University, London, UK, July 9 th, 2014

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

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

Photon Number Resolution ◙ Photon Number Resolution & Excess Noise Factor l 10 el 0 pix Idea SSP M Ide al ph oto nd ete cto r Burgess variance theorem Sergey Vinogradov o. PAC Advanced School on Accelerator Optimization Royal Holloway University, London, UK, July 9 th, 2014

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 Random Photo events Random primary (Photo) events Non-random (Dark) event Primary … Ra nd om CT eve nts 1 st CT 2 nd CT Non-random (Dark) event Sergey Vinogradov Random primary (Photo) events … … … o. PAC Advanced School on Accelerator Optimization Random CT events … Random CT events Branching Poisson Crosstalk Process … Random CT events Geometric Chain Afterpulsing Process … … … Royal Holloway University, London, UK, … July 9 th, 2014

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

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 A few photon detection spectrum of Hamamatsu MPPC (S. Vinogradov, NDIP 2011; experiment B. Dolgoshein et al. , NDIP 2008) (S. Vinogradov, SPIE 2012) Sergey Vinogradov o. PAC Advanced School on Accelerator Optimization Royal Holloway University, London, UK, July 9 th, 2014

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

Si. PM 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 M. Grodzicka, NSS 2011 Recovery non-linearity → ENF S. Vinogradov et al. , IEEE NSS/MIC 2009 2) 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 o. PAC Advanced School on Accelerator Optimization Royal Holloway University, London, UK, July 9 th, 2014

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

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

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 o. PAC Advanced School on Accelerator Optimization Royal Holloway University, London, UK, July 9 th, 2014

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

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

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 m. V 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 o. PAC Advanced School on Accelerator Optimization Royal Holloway University, London, UK, July 9 th, 2014

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

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

Si. PM trends and advances ◙ Market leaders Hamamatsu KETEK Sens. L FBK / Advan. Si. D Excelitas / Perkin Elmer Philips (digital Si. PM) ◙ 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/mm 2) Faster SER, smaller pixel size (25 → 10 um) Larger area, larger arrays (3 x 3→ 10 x 10 mm 2, 4 x 4 → 16 x 16 channels) Latest news from 2 nd Si. PM Advanced Workshop and Conf. on New Development s in Photodetection, 2014 Sergey Vinogradov o. PAC Advanced School on Accelerator Optimization Royal Holloway University, London, UK, July 9 th, 2014

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

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

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

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

Sens. L ◙ Fast capacitive output FWHM < 3. 2 ns @ 6 x 6 mm 2 ◙ Large arrays / modules ◙ Low cost Sergey Vinogradov o. PAC Advanced School on Accelerator Optimization Royal Holloway University, London, UK, July 9 th, 2014

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

Summary on BLM with Si. PM ◙ BLM is one of the most challenging application for Si. PM 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 Si. PM output signal processing ◙ BLM with Si. PM: big problem with a chance to win And with a lot of space for new ideas, designs, and fun Sergey Vinogradov Seminars on Si. PM at the Cockcroft Institute 3 February 2014 49

Summary on Si. PM ◙ Si. PM 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 Si. PM applications Scintillation Cherenkov Laser pulse And much more… Sergey Vinogradov o. PAC Advanced School on Accelerator Optimization Royal Holloway University, London, UK, July 9 th, 2014

The end ◙ Thank you for your attention ◙ Questions? Sergey. Vinogradov@liv. ac. uk Sergey Vinogradov o. PAC Advanced School on Accelerator Optimization Royal Holloway University, London, UK, July 9 th, 2014 51