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Tracking particles in space and time

Explore the use of timing information in particle tracking experiments and the development of silicon sensors for time-tracking. Discover the benefits of incorporating timing data in event reconstruction and the challenges of implementing timing layers in tracking systems.

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Tracking particles in space and time

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  1. Tracking particles in space and time Besides a few indirect signals of new physics, particle physics today faces an extraordinary drought. We need to cross an energy- cross section desert to reach the El-dorado of new physics. Very little help in the direction of this path is coming from nature, the burden is on the accelerator and experimental physicists to provide the means for this crossing. Timing is one of the enabling technologies to cross the desert People of Silicon Nicolo Cartiglia, INFN, Torino – Tracking in 4D The journey to new physics across the LHC desert

  2. The effect of timing information • The inclusion of track-timing in the event information has the capability of changing radically how we design experiments. • Timing can be available at different levels of the event reconstruction, in increasing order of complexity: • Timing in the event reconstruction Timing layers • this is the easiest implementation, a layer ONLY for timing • Timing at each point along the track  4D tracking • tracking-timing • Timing at each point along the track at high rate  5D tracking • Very high rate represents an additional step in complication, very different read-out chip and data output organization Nicolo Cartiglia, INFN, Torino – Tracking in 4D

  3. One sensor does not fit all • Silicon sensors for tracking come in many shapes, fitting very different needs: • Spatial precision: from a few microns to mm (pixels, strips) • Area: from mm2 up to hundred of square meter • Radiation damage: from nothing to >1E16 neq/cm2 (3D, thin planar, thick planar) • Likewise, Silicon sensors for time-tracking are being developed to fit different needs with respect of time and space precision. The geometries above are combined with: • Very high time precision ~ 30-50 ps per plane • Good time precision ~ 50-100 ps per plane • Currently two large projects, UFSD and Timespot, are developing Silicon sensors for time-tracking. Nicolo Cartiglia, INFN, Torino – Tracking in 4D

  4. Preamble: simulator Weightfield2 Available at: http://personalpages.to.infn.it/~cartigli/Weightfield2/Main.html It requires Root build from source, it is for Linux and Mac. It will not replace TCAD, but it helps in understanding the sensors response Nicolo Cartiglia, INFN, Torino – Tracking in 4D

  5. Weightfield2 • Highlights: • It is completely open source • it’s fast • It generates the signal from several sources (MIP, alpha, lasers..) • Runs in batch mode writing output files • It loads/save configurations • It has basics electronics simulation • It crashes occasionally • How to use it: • Obtain the last version from • http://personalpages.to.infn.it/~cartigli/Weightfield2/Main.html • 1)From the download page, get the latest version • 2)Unzip it and then type: • 3)Make or 3-bis) make -f Makefile_MacOS10.13_root6 • 4)./weightfield Nicolo Cartiglia, INFN, Torino – Tracking in 4D

  6. Current situation at LHC: no real need for timing Nicolo Cartiglia, INFN, Torino – Tracking in 4D

  7. Is timing really necessary at HL-LHC? The research into 4D tracking is strongly motivated by the HL-LHC experimental conditions: 150-200 events/bunch crossing Nicolo Cartiglia, INFN, Torino – Tracking in 4D • According to CMS simulations: • Time RMS between vertexes: 153 ps • Average distance between two vertexes: 500 um • Fraction of overlapping vertexes: 10-20% • Of those events, a large fraction will have significant degradation of the quality of reconstruction At HL-LHC: Timing is equivalent to additional luminosity

  8. One extra dimension: tracking in 4Dimension Nicolo Cartiglia, INFN, Torino – Tracking in 4D Timing complements tracking in the correct reconstruction of the events

  9. Timing at each point along the track • Massive simplification of patter recognition, new tracking algorithms will be faster even in very dense environments • Use only “time compatible points” Nicolo Cartiglia, INFN, Torino – Tracking in 4D protons Z- Vertex distribution protons Timing

  10. Where do we place a single timing layer? The tracking community thinks it is a wonderful idea, clearly to be implemented outside the tracker volume, in front of the calorimeter The calorimeter community thinks it is a wonderful idea, clearly to be implemented far from the calorimeter, in the tracker volume Nicolo Cartiglia, INFN, Torino – Tracking in 4D We are now in contact with the muon community….

  11. Silicon time-tagging detector (a simplified view) Nicolo Cartiglia, INFN, Torino – Tracking in 4D Time is set when the signal crosses the comparator threshold The timing capabilities are determined by the characteristics of the signal at the output of the pre-Amplifier and by the TDC binning. Strong interplay between sensor and electronics

  12. Good time resolution needs very uniform signals Signal shape is determined by Ramo’s Theorem: Drift velocity Weighting field Nicolo Cartiglia, INFN, Torino – Tracking in 4D The key to good timing is the uniformity of signals: Drift velocity and Weighting field need to be as uniform as possible Basic rule: parallel plate geometry

  13. Time resolution total current total current Subleading, ignored here parallel plate geometry electron current electron current hole current hole current Time walk: Amplitude variation, corrected in electronics Shape variations: non homogeneous energy deposition Usual “Jitter” term Here enters everything that is “Noise” and the steepness of the signal Nicolo Cartiglia, INFN, Torino – Tracking in 4D Need large dV/dt

  14. Signal formation in silicon detectors We know we need a large signal, but how is the signal formed? What is controlling the slew rate? Nicolo Cartiglia, INFN, Torino – Tracking in 4D • A particle creates charges, then: • The charges start moving under the influence of an external field • The motion of the charges induces a current on the electrodes • The signal ends when the charges reach the electrodes

  15. What is the signal of one e/h pair? (Simplified model for pad detectors) Let’s consider one single electron-hole pair. The integral of the current is equal to the electric charge, q: + + - - However the shape of the signal depends on the thickness d: thinner detectors have higher slew rate Nicolo Cartiglia, INFN, Torino – Tracking in 4D Thin detector i(t) Thick detector D t • One e/h pair generates higher current in thin detectors d Weighting field

  16. Large signals from thick detectors? (Simplified model for pad detectors) Thick detectors have higher number of charges: d + + + + + + + - - - - - - - However each charge contributes to the initial current as: Nicolo Cartiglia, INFN, Torino – Tracking in 4D The initial current for a silicon detector does not depend on how thick (d) the sensor is: D Number of e/h = 75/micron velocity • Initial current = constant Weighting field

  17. Summary “thin vs thick” detectors (Simplified model for pad detectors) Thin detector d + + + + + + + - - - - - - - S Thick detector Nicolo Cartiglia, INFN, Torino – Tracking in 4D i(t) D Thick detectors have longer signals, not higher signals We need to add gain

  18. Gain needs E ~ 300kV/cm. How can we do it? 1) Use external bias: assuming a 50 micron silicon detector, we need Vbias = ~ 600 - 700 V Difficult to achieve Nicolo Cartiglia, INFN, Torino – Tracking in 4D 2) Use Gauss Theorem: E = 300 kV/cm  q ~ 1016 /cm3 Need to have 1016/cm3 charges !!

  19. Gain in Silicon detectors • Gain in silicon detectors is commonly achieved in several types of sensors. It’s based on the avalanche mechanism that starts in high electric fields: V ~ 300 kV/cm Chargemultiplication • Gain: • = strong E dependance • ~ 0.7 pair/μm for electrons, • ~ 0.1 for holes DV ~ 300 kV/cm Nicolo Cartiglia, INFN, Torino – Tracking in 4D Concurrent multiplication of electrons and holes generate very high gain - - + + + - - + - + + - - - - - + - - + - - + + + - + + - - - • Silicon devices with gain: • APD: gain 50-500 • SiPM: gain ~ 104

  20. Electric fields in Silicon sensors Gain happens when the Efield is near the critical values, 300 kV/cm 3 methods to increase Efield: Doping in the bulk Doping in the gain layer Bias Nicolo Cartiglia, INFN, Torino – Tracking in 4D • The “low gain avalanche diode” offers the most stable situation • Gain due to interplay between gain layer and bias

  21. Standard vs Low Gain Avalanche Diodes Nicolo Cartiglia, INFN, Torino – Tracking in 4D The LGAD sensors, as proposed and manufactured by CNM (National Center for Micro-electronics, Barcelona): High field obtained by adding an extra doping layer E ~ 300 kV/cm, closed to breakdown voltage

  22. Gain layer position: a detail of great importance A B C D.1 D.2 Different producers use different designs, implanting the gain layer at different depth n pad 0.5 0.7 1.5 2.3 Depth [um] In a parallel plate capacitor, the field E does not depend on the distance d, only on the charge Q Nicolo Cartiglia, INFN, Torino – Tracking in 4D Q2 Q1 E2 E1 d1 d2 Gain: exp(field * distance) If Q1 = Q2, then E1 = E2  If depth increases, doping should decrease to keep the same gain

  23. How gain shapes the signal Gain electron: absorbed immediately Gain holes: long drift home + - Initial electron, holes + - + - Nicolo Cartiglia, INFN, Torino – Tracking in 4D • Electrons multiply and produce additional electrons and holes. • Gain electrons have almost no effect • Gain holes dominate the signal •  No holes multiplications

  24. Interplay of gain and detector thickness The rate of particles produced by the gain does not depend on d (assuming saturated velocity vsat) Particles per micron Gain + - Gain v Nicolo Cartiglia, INFN, Torino – Tracking in 4D  Constant rate of production However the initial value of the gain current depends on d (via the weighing field)  Gain current ~ 1/d A given value of gain has much more effect on thin detectors

  25. Gain current vs Initial current  Go thin!! (Real life is a bit more complicated, but the conclusions are the same) Full simulation (assuming 2 pF detector capacitance) Nicolo Cartiglia, INFN, Torino – Tracking in 4D 300 micron: ~ 2-3 improvement with gain = 20 !!! Significant improvements in time resolution require thin detectors

  26. Gain and Signal current i(t) medium Nicolo Cartiglia, INFN, Torino – Tracking in 4D thick thin t3 t2 t1 t The rise time depends only on the sensor thickness ~ 1/d

  27. Ultra Fast Silicon Detectors UFSD are LGAD detectors optimized to achieve the best possible time resolution Specifically: Thin to maximize the slew rate (dV/dt) Parallel plate – like geometries (pixels..) for most uniform weighting field High electric field to maximize the drift velocity Highest possible resistivity to have uniform E field Small size to keep the capacitance low Small volumes to keep the leakage current low (shot noise) Nicolo Cartiglia, INFN, Torino – Tracking in 4D

  28. Alternatives Silicon Sensors for timing • 3D sensors for timing  Timespot • Resistive Silicon Detectors • Monolithic timing sensors Nicolo Cartiglia, INFN, Torino – Tracking in 4D

  29. 3D sensors for timing applications 3D sensors enjoy good performance even at fluences f ~ 1016 n/cm2 Can they be used in 4D-tracking? Can diamond 3D work? ++++++ ----- In their “column” geometry, they cannot, the Efield is not uniform enough E field • However, using trenches gives a parallel plate geometry, and a weighting field ~ 1/d • Insensitive to non-uniform charge deposition GOOD! • Challenges: • - Position dependent current shape • - Strong signal reduction with irradiation Nicolo Cartiglia, INFN, Torino – Tracking in 4D E field

  30. Nicolo Cartiglia, INFN, Torino – Tracking in 4D

  31. Pulse shape in 3D – trench detectors Assume: 3D silicon, 50 micron separation, 200 micron thick 200 150 100 50 200 150 100 50 Edge hit, f = 1016 n/cm2 Tot Q = 0.7 fC Edge hit, new ToT Q = 2.2 fC Different signal length Nicolo Cartiglia, INFN, Torino – Tracking in 4D 0 10 20 30 40 [micron] 0 10 20 30 40 [micron] Central hit, new ToT Q = 2.2 fC Central hit, f = 1016 n/cm2 ToT Q = 1 fC

  32. Nicolo Cartiglia, INFN, Torino – Tracking in 4D

  33. Nicolo Cartiglia, INFN, Torino – Tracking in 4D

  34. Resistive electrode The signal is frozen on the resistive sheet, and it’s AC coupled to the electronics  100% fill factor Segmentation is achieved via AC coupling AC coupling n++ electrode Nicolo Cartiglia, INFN, Torino – Tracking in 4D gain layer p++ electrode The AC read-out sees only a small part of the sensor: small capacitance and small leakage current.

  35. Monolithic technology Nicolo Cartiglia, INFN, Torino – Tracking in 4D

  36. Physical limit to time precision: Non-Uniform Energy deposition • Fluctuations in ionization cause two major effects: • Amplitude variations, that can be corrected with time walk compensation • For a given amplitude, the charge deposition is non uniform. • These are 3 examples of this effect: Nicolo Cartiglia, INFN, Torino – Tracking in 4D

  37. UFSD time resolution summary • The UFSD advances via a series of productions. • For each thickness, the goal is to obtain the intrinsic time resolution • Achieved: • 20 ps for 35 micron • 30 ps for 50 micron Resolution without gain Nicolo Cartiglia, INFN, Torino – Tracking in 4D UFSD1 UFSD2, 3

  38. UFSD time resolution UFSD from Hamamatsu: 30 ps time resolution, Value of gain ~ 20 Nicolo Cartiglia, INFN, Torino – Tracking in 4D

  39. UFSD group: FBK – Trento Uni – INFN-To UFSD1: 300-micron. First LGAD production at FBK. Gain layer study, edges UFSD2: 50-micron. Very successful, good gain and overall behavior, excellent time resolution. Gain layer doping: Boron, Gallium, Boron + Carbon, Gallium+Carbon UFSD3: 50-micron, produced with the stepper, many Carbon levels, small dead space Nicolo Cartiglia, INFN, Torino – Tracking in 4D UFSD3 UFSD2

  40. Sensitivity to gain uniformity • Gain requires very accurate manufacturing capabilities: • +3% doping doubles the collected charge • -4% halves the collected charge The bias can be adjusted to keep the charge constant as the doping in the GL changes. Nicolo Cartiglia, INFN, Torino – Tracking in 4D

  41. Irradiation effects • Irradiation causes 3 main effects: • Decrease of charge collection efficiency due to trapping • Doping creation/removal • Increased leakage current, shot noise • We need to design a detector that is able to survive large fluences, up to ~ 5E15 – 1E16 neq/cm2 Nicolo Cartiglia, INFN, Torino – Tracking in 4D Is the damage in Silicon increasing for ever?

  42. Damage in Silicon saturates? As you have seen in the previous lectures, damage in Silicon is very complex, due to a distributed disruption of the lattice Simulation of 1 MeV neutron interaction Nicolo Cartiglia, INFN, Torino – Tracking in 4D Damage looks like a tree, with clusters ~ 10 Å long along the track

  43. Clusters superposition At fluences high enough, clusters will develop on top of each other, and damage will happen on already damaged Silicon. Clusters superposition happens in space, and a full simulation is needed to evaluate it correctly. Let’s made a 2D approach instead Nicolo Cartiglia, INFN, Torino – Tracking in 4D

  44. 2D calculations of superposition • Define a particle hit on the surface by a small square ao (for example 1 Å2 ) • Calculate the probability for the (n+1)th particle to hit an empty square Probability of hitting an empty square of area 1 Å2 Nicolo Cartiglia, INFN, Torino – Tracking in 4D 1) Probability for a particle to hit a square = ao/ cm2 = 1E-18 2) Probability for a particle to miss a square: 1 – 1E-18 3) Probability for a particle to hit a square that has been missed by the previous n = (1 – 1E-18)n 4) This is a Poisson probability problem, with parameter ao We don’t know at what fluence, but we can expect to see a slowing down of damage at high fluences.

  45. (1) Charge Collection efficiency Traditionally, the e/h lifetimes decreases as a function of the fluence: ) • For some unknown reason, after an initial decrease, the lifetime in thin sensors remains constant Saturation? Nicolo Cartiglia, INFN, Torino – Tracking in 4D This is a common problem in our understanding of how Silicon sensors work: Silicon sensors after heavy irradiation behaves better than what is expected by extrapolating from lower fluence data

  46. 2) Initial acceptor creation/removal Density of the boron doping vs irradiation: Initial acceptor removal Deep level creation p-doping lookalike Nicolo Cartiglia, INFN, Torino – Tracking in 4D

  47. Acceptor creation Deep level creation Linear with fluence Nicolo Cartiglia, INFN, Torino – Tracking in 4D Saturation of damage?

  48. Acceptor removal Unfortunate fact: irradiation de-activate p-doping removing Boron from the reticle Boron Radiation creates interstitial defects that inactivate the Boron: Si_i + B_s  Si_s + B_i Nicolo Cartiglia, INFN, Torino – Tracking in 4D Two possible solutions: 1) use Gallium, 2) Add Carbon Gallium From literature, Gallium has a lower possibility to become interstitial Carbon Interstitial defects filled with Carbon instead of with Boron and Gallium

  49. Is the Boron still there? Nicolo Cartiglia, INFN, Torino – Tracking in 4D Yes, the Boron is still there, but it is not active any more… Instead of being “substitutional” (i.e. in the place of a Silicon atom) is “interstitial” (i.e. In the middle of the lattice, not electrically active)

  50. Acceptor removal data Acceptor removal coefficient Puzzle: the removal of acceptor depends on their density the removal is slower for higher densities Nicolo Cartiglia, INFN, Torino – Tracking in 4D

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