A TCAD based model of double metal layer effects and a
review of the radiation damage and monitoring of LHCb Velo
21.02.2018
Akademia Górniczo-Hutnicza im. Stanisława Staszica w Krakowie
AGH University of Science and Technology
Maciej MajewskiOn behalf of LHCb VELO group
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LHCb – Experiment
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[Int. J. Mod. Phys. A 30 (2015)]
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Single-arm spectrometer, fully instrumented in pseudo rapidity range
𝟐 < 𝜼 < 𝟓 (solid angle coverage ~ 𝟒%, 𝟒𝟎% B mesons)
High performance tracking system (critical!)
Spatial resolution ~𝟒 𝝁𝒎 at vertex detector
𝜟𝒑
𝒑= (𝟎. 𝟒 - 𝟎. 𝟔)% for tracks with momentum in range p → (5 -
100) 𝐺𝑒𝑉
Impact parameter resolution ~𝟐𝟎 𝝁𝒎 for high 𝑝𝑇 tracks
Decay time resolution ~𝟒𝟓 𝒇𝒔 (𝐵𝑠 → 𝐽/𝜓𝜑)
Primary vertex resolution 𝜎𝑥,𝑦 ≈ 13 𝜇𝑚 and 𝜎𝑧 ≈ 80 𝜇𝑚 @25
tracks
Excellent particle identification capability
LHCb – Experiment
VELO – VErtex LOcator
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VELO consists of two
retractable halves
They enclose the beam
collision area
They move to as close
as 7 mm to the beam
[JINST 9 (2014) P09007]
VELO - modules
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There are 21 modules in each half
Modules have an R- and Phi-type sensor
Each sensor consists of 2048 silicon strips
About 170 000 readout channels
VELO monitoring - Lovell
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Lovell – VELO Monitoring Platform – Architecture
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[J.Phys.Conf.Ser. 898 (2017) no.9]
Lovell – VELO Monitoring Platform – GUI
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Lovell – VELO Monitoring Platform – GUI
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Why we need Intelligence in VELO
There is 170 000 readout channels that need to be monitored
The state of detector is not constant (temperature irradiation)
The calibration process is time consuming (and must conducted
without beam)
Intelligence example
Calibration simulation (and anomaly detection) with Machine
Learning. ADC threshold for clustering per channel.
We learn some model parameters (per n-th channel)
𝑇′𝑛~ 𝐺𝑎𝑢𝑠𝑠𝑖𝑎𝑛(𝜇 = (𝑥 ∗ 𝜇′𝑛 + 𝜇𝑛), 𝜎 = (𝑥 ∗ 𝜎′𝑛 + 𝜎𝑛))
Created artificial metric for calibration assessment, and assigned to
callibration runs
Lovell – VELO Monitoring Platform – Intelligence
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Lovell – VELO Monitoring Platform – Intelligence
Radiation Damage
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Charge Collection Efficiency Scans used to measure
Effective Depletion Voltage
Every fifth module is studied.
We record and reconstruct the
tracks.
Tracks that hit studied module,
and 4 neighbouring modules are
used.
All other modules except the
studied ones are in nominal
voltage
ADC counts are fitted with
LanGauss model to find the
MPV
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(year 2013)
Charge Collection Efficiency Scans used to measure
Effective Depletion Voltage
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- Effective depletion
voltage is 80% of the
maximum (plateau)
(year 2013)
Charge Collection Efficiency Scans used to measure
Effective Depletion Voltage
Overlay of Hamburg
model
Measurement of EDV for
different sensors and
different regions
Inputs include
Temperature and
Luminosity
EDV for October 2017
(close to ℒ = 7𝑓𝑏−1)
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p-on-n
Assuming similar conditions as in previous years we can use Hamburg
model to forecast the EDV for the future.
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Charge Collection Efficiency Scans used to measure
Effective Depletion Voltage
Prediction of Hamburg model
Charge Collection Efficiency Scans used to measure
Effective Depletion Voltage
Hardware limit is
500 V, we are still
well below that
threshold
Need to monitor
carefully the
situation in 2018
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Double layer effect - TCAD
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Double Layer Effect – Experiment
All sensors need two metal lines
First metal layer to capacitively couple the silicon strips (implant is along the full
length of each strip)
Second metal layer carrying signal to the amplifier on top of the outer strips
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Double Layer Effect – Experiment
Cluster Finding Efficiency
for R sensor, you can see
that finding efficiency is
much higher in places of
gaps in second metal layer
The second year of data
taking, after about 600 𝑝𝑏−1
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Satellite peaks clearly visible – effect
increase with the accumulated luminosity
Double Layer Effect – Experiment
Second metal layer for R sensor. (Metal is darker blue)
Notice the gaps between the routing lines.
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Double Layer Effect – Experiment
Cluster finding efficiency 2D map, after about 5 𝑓𝑏−1
Phi-sensor R-sensor
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Double Layer Effect – Experiment
Cluster finding efficiency for given
distance to a Routing Line, and
distance to the nearest strip
You can see that further from Routing
Line the Cluster Finding Efficiency is
larger
Also, the CFE increases as the
distance to the nearest strip decreases
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Double Layer Effect – Explanation?
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Clearly radiation damage induced effect, however:
Insensitive to higher bias voltage
After some time, the effect seems to be saturated and remain stable
Can explain in a sensible way if we assume that:
Before the irradiation the free surface charges have some mobility
and can act as an effective shielding that prevents the routing lines
to couple to the moving charge
After irradiation the positive space charge in the oxide insulator
starts to trap the electrons that can no longer act as a shield
We start to see correlated fake hits in the inner strips!
Let’s build a TCAD model and try to reproduce the measurements!
Double layer effect - TCAD
A TCAD model of geometry of the strips and routing lines
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The Purpose
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The main point of the simulation is to build a model that could be used to
measure/estimate the CCE
We have a number of competing effects:
Acceptors on 𝑆𝑖/𝑆𝑖𝑂2 interface, surface defects (both affecting electron
mobility – the shielding) and bulk defects (charge trapping) – decrease the
CCE
Space charge of the oxide layer increases and acts as a shield – increase
the CCE
The right parameters are of paramount importance!
Hard to figure out the p-spray doping profile and depth
Also positive charge concentration in the damaged oxide
Need to make some educated guesses and playing with parameter scans
The initial results are very promising!
Double layer effect - TCAD
We use TCAD + Routing Line Geometry to model Charge Collection
Efficiency
Measured CFE Simulated CCE
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Summary
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Monitoring
We have dedicated software for data monitoring
Incorporation of intelligence to the detector monitoring will enhance the
capabilities of data taking
Radiation damage
The radiation damage of the detector well under control, and should not
impact data taking in 2018
Double layer effect
This effect has great effect on cluster finding efficiency
TCAD model is helpful to understand this proces
This knowledge has impact on silicon sensor design
Thank you for attention.
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Backup slides:
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NEW
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High threshold modelling
𝑇𝑛
n
𝑇𝑛~ 𝐺𝑎𝑢𝑠𝑠𝑖𝑎𝑛(𝜇 = 𝜇𝑛, 𝜎 = 𝜎𝑛)
𝑛 ∈ 0,1983
- Threshold value for channel number n (only „good”
callibrations!!)
(excluded header-crosstalk)
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𝑇′𝑛~ 𝐺𝑎𝑢𝑠𝑠𝑖𝑎𝑛(𝜇 = (𝑥 ∗ 𝜇′𝑛 + 𝜇𝑛), 𝜎 = (𝑥 ∗ 𝜎′𝑛 + 𝜎𝑛))
Total dataset
Calibration date X
2011-03-07 1
2012-08-02 3
2012-07-30 10
2012-08-01 10
26 others (already
calculated)
0
𝑇𝑛~ 𝐺𝑎𝑢𝑠𝑠𝑖𝑎𝑛(𝜇 = 𝜇𝑛, 𝜎 = 𝜎𝑛)
