The Effect of Highly Ionising Particles on the APV25 Readout Chip R. Bainbridge on behalf of the CMS Tracker collaboration Colmar, 11th September 2002

Highly Ionising events Interactions between incident particles and silicon sensors can produce Highly Ionising Particles (i.e, recoiling nuclei and/or nuclear fragments) HIP events result in signals equivalent of up to 1000 minimum ionising particles, which can saturate the FE electronics and result in signal loss Estimate of +Si interaction rate in the CMS Tracker: (+p)  200 mb  (+Si)  2.6 barn (E = 300 MeV)  mean free path (+ in Si) = 7.7 cm  rate  5  10-3 per BX per plane per % occupancy Rate at which these interactions saturate the FE electronics?… Duration of this saturated state?… Resulting inefficiency?…

First observation of the HIP effect X5 test beam (Oct 2001) saw 6 TOB modules exposed to 120 GeV pion beam with 25 ns bunch structure APV analogue data exhibit large (truncated) signals and suppressed output in all other channels (i.e, negative common mode shifts) A sufficiently large signal will fully suppress the output of all 128 channels  APV insensitive to MIP signals “Deadtime” is the period for which an APV is insensitive to MIP signals Raw analogue output from 6 modules (4 APV25s per module) Disabled APV + activity downstream

Origin of problem Inverter stage powering scheme: All 128 inverter FETs draw from V250 via external hybrid resistor, RINV On-chip CM subtraction (VR  VCM) Large signals drive down output on all other channels Inverter FETs seeing HIP signal draw largest current possible  VR pulled down  output of all channels suppressed Output remains suppressed until signal dissipates Possible solution: Reduce RINV from 100  to 50   more current available at inverter stage (reduces effect of large signals pulling down VR)

Identifying HIP events Large signals result in negative tail in CM distribution Peak at -150 is a consequence of limited APV dynamic range APV still sensitive to MIP signals for intermediate CM shifts Only fully suppressed baselines result in deadtime HIP events that result in deadtime are identified by: CM  -140 & Qclu  500 CMMAX -140 500 Common mode distribution [ADC counts] Cluster charge distribution for CM  -150 [ADC counts]

HIP rate measurement (X5) “HIP rate” = rate at which +Si interactions result in deadtime (not rate of +Si interactions!) HIP rate [per pion per plane] = NHIPs Ntriggers  Nplanes  Multiplicity Beam Ntriggers Nplanes Multiplicity NHIPs Rate ( stat. error) Pion 247500 6 1.9 1051 (3.7  0.1) · 10-4 288077 5 2.0 1142 (4.0  0.1) · 10-4 Muon 225000 7 (3.3  1.2) · 10-6

X5 beam structure 25 ns bunch structure, with 924 RF buckets per SPS orbit (23.1 s) Pions delivered to X5 area in trains of ~60 consecutive RF buckets 1 train per SPS orbit  trains spaced by empty gaps of ~22 s Triggers were distributed uniformly throughout train (for these particular run conditions / trigger requirements) SPS orbit = 23.1 s (924 RF buckets) 1 train per SPS orbit X5 Train length  60  25 ns Distribution of triggers in trains

HIP rate measurement (X5) Distribution of HIP events in particle trains Alternative HIP rate measurement: Normalise distribution of “HIP events” to trigger distribution to obtain: “probability of trigger containing HIP event” as function of trigger position in train Probability of trigger containing HIP event is constant: ~4  10-3  4  10-4 per pion per plane Probability of trigger containing HIP event as function of trigger position

Identifying disabled APVs APVs experiencing deadtime can also be identified: If a trigger shortly follows a HIP event, then an APV may be read out when in a saturated state No signal and pedestal structure are observed in APVs experiencing deadtime  criteria for selecting disabled APVs: CM  -140 ADC counts, spread  10 ADC counts Spread = ADCMAX – ADCMIN 0 25 50 75 100 125 150 175 200 Spread [ADC counts] Peak at ~70 ADC counts and tail due to MIP signals Disabled APVs

Deadtime measurement (X5) Normalise distribution of “disabled APVs” to trigger distribution to obtain: “probability of trigger containing disabled APV” as function of trigger position in train Probability of disabled APV is small at start of train as cannot be HIP events in preceding RF buckets Probability saturates later in train due to finite deadtime “Risetime” provides measure of average deadtime: ~300 ns Distribution of disabled APVs in trains Deadtime ~300 ns

Laboratory deadtime measurements Measure sensitivity of APV25 to normal signals after simulated “HIP” Inject & measure amplitude of MIP signal Sweep injection time of “HIP” signal t Latency MIP signal Trigger on MIP signal Vary HIP injection time Deadtime as a function of HIP signal (Edep) Response to MIP signal after HIP Deadtime Measured deadtime  350 ns Threshold Edep ~10 MeV required before deadtime is observed

Simulation* of pion-Si interactions Cumulative energy deposition spectrum Differential energy deposition spectrum X5 data provide no information on signal magnitude and spatial distribution  simulation required Predicts Edep up to ~100 MeV Probability of Edep  10 MeV is ~10-3 Predicts comparable HIP rates for X5 and CMS Only inelastic collisions contribute significantly to P(Edep  1 MeV)  negligible HIP rate with muons * CMS NOTE 2002/011 (M. Huhtinen)

PSI beam test Aims for dedicated HIP study: Verification of previous HIP rate measurements at X5 Observation of full APV25 recovery after HIPs to allow direct deadtime measurement Rate / deadtime measurements with reduced RINV values Hardware/trigger requirements for PSI Trigger logic providing particle-vetoes before triggers (clean measurements) Trigger card providing multiple triggers (every 75 ns) + APV25 multi-mode operation (peak mode readout, 3 frames per trigger)  data every 25 ns for 750 ns after initial trigger 6 TOB + 3 TIB + 3 TEC modules exposed to 300 MeV pion beam

HIP rate measurement (PSI) Criteria for selecting HIPs as for X5 Rates in agreement with simulation and X5 results Observe factor ~0.6 between rates for 500 m and 320 m sensors Slightly reduced HIP rate for lower values of RINV Type RINV [] HIP rate 320 m thick sensors: TIB 50 (2.3 ± 0.1) 10-4 TIB 100 (3.7 ± 0.1) 10-4 500 m thick sensors: TEC 100 (6.2 ± 0.1) 10-4 TOB 50 (5.5 ± 0.1) 10-4 TOB 75 (5.8 ± 0.2) 10-4 TOB 100 (6.2 ± 0.1) 10-4 Preliminary

Example of APV25 recovery Peak (raw data) Top: raw analogue data (peak mode) Bottom: numerical deconvolution of raw data 350 ns 325 ns 375 ns 275 ns 200 ns 225 ns 250 ns 400 ns 300 ns 425 ns 575 ns 600 ns 625 ns 650 ns 550 ns 525 ns 450 ns 475 ns 500 ns 175 ns 125 ns 25 ns 0 ns 150 ns 50 ns 75 ns 100 ns 300 t = 0 ns: HIP event t = 125 ns: Baselines begin to recover t = 200 ns: Baseline flat in deconvolution mode Digital zero t = 275 ns: Nominal baseline position in decon mode Continued slow recovery in peak mode 100 Deconvolution (numerical calc) 200 t = 650 ns: Baseline still recovering in peak mode Slow time-varying signals filtered out by deconvolution algorithm  improved baseline behaviour (~flat and shorter recovery period) Nominal baseline pos.

Baseline recovery Average baseline position as function of time after HIP (peak mode) Curves grouped according to module type and resistor value Modules equipped with lower RINV values recover first Expect quicker recovery and no overshoot in decon mode 0 125 250 375 500 625 750 Time [ns] Baseline position Nominal position Fully saturated Disabled APVs?

Inefficiency due to HIP effect “Hit reconstruction efficiency in hipped APV” as function of time after HIP event 0 100 200 300 400 500 600 700 Time [ns] 1.0 0.8 0.6 0.4 0.2 0.0 Efficiency -60 < CM < -30 -90 < CM < -60 -30 < CM < 0 -30 < CM < 0 Simulation provides probability of a given energy deposition, P(E) Deadtime dependence on energy, (E), provided by lab measurements Resulting inefficiency (per plane per % occupancy) is sub-% level Less than inefficiency due to unbonded or noisy strips Using PSI data, perform efficiency scans for various CM shifts and convert into “effective deadtime”, ’(CM) Calculate probability of observing CM shift, P’(CM), from PSI data Inefficiency again sub-% level Preliminary

Conclusions Large signals, resulting from rare interactions between incident hadrons and silicon sensors, can saturate the FE electronics and result in inefficiencies (per plane per % occupancy) at sub-% level Encouraging that we are investigating effects in the readout chain that affect the performance of the Tracker at 10-3 level