Download Silicon detectors for particle physics laboratory

Silicon detectors for particle physics laboratory
R. Bates and L. Eklund from Glasgow University
J. Garcia from Geneva University
This laboratory would not have been possible without the financial help from the particle
physics groups of the Universities of Geneva and Glasgow and the sponsorship from the
Scottish Universities Physics Alliance (SUPA) and the Science and Technology Facilities
Council, UK.
General Introduction
Semiconductor theory
A semiconductor is a solid whose electrical conductivity can be controlled by doping; adding
impurities. It has an electrical resistivity between that of a conductor and insulator. The
material is characterised by an energy band diagram with an energy gap between the
conduction and valance bands of a few electron volts (more than 4 eV the material is said to
be an insulator). The current in a semiconductor is carried by a flow of electrons and or by
positively charged particles called ‘holes’ in the electron structure of the material. The
electrons are free to conduct (know as free electrons) when they inhabit the conduction
band. This takes place due to thermal excitation or doping. When an electron is excited from
the valance band to the conduction band it leaves behind a hole in the valance band; which
can be treated as a charge carrier with positive charge.
The conductivity of the material is controlled by the impurity concentration. Silicon is a
group IV element with four valance electrons. Doping with a group V material (with 5
valence electrons) introduces an extra electron into the crystal structure which inhabits the
conduction band. This is known as n-type doped material. Similarly doping with a group III
element (three valence electrons) introduces a lack of an electron, or a hole, into the
material. This is known as p-type material. A simple diode structure, called a p-n junction, is
formed when a piece of p-type and a piece of n-type doped material are brought together.
In the p-n junction electrons diffuse, due to the carrier concentration difference, from the ntype material to the p-material leaving a net positive fixed charge in the n-type region. The
electrons recombine with the holes in the p-type material. Likewise free holes diffuse from
the p-type material to the n-type, recombining with electrons, leaving a region of negative
fixed charge in the p-type material. The fixed positive charge causes a force on the charge
carriers and creates a drift current that is opposite in direction to the diffusion current.
Eventually a steady state is reached. The region of fixed space charge is known as the
depletion region as it is depleted (free of) mobile charge carriers. Due to the fixed charge a
potential is present over the depletion region, and therefore an electric field exists. The
application of a reverse bias over the p-n junction causes the depletion region to grow and
eventually the material will become fully depleted, i.e. leading to a region free from mobile
charge carriers.
For a silicon detector a p+-i-n+ structure is typically used. The intrinsic material (denoted by
an i) is in fact either very slightly n or p type (denoted as n- and p-). The positive superscript
signifies that the doping concentration is high. The doping concentrations are typically 1018
atoms cm-3 for the n+ and p+ type doped sections and 1011 atoms cm-3 for the near intrinsic
region; while intrinsic silicon has a doping level of 1.5 x 1010 atoms cm-3 at room
temperature. The detector’s doped areas can be segmented into strips or pixels to enable
position sensitivity. The heavily doped regions are only a few microns in thickness while the
near intrinsic region is typically hundreds of microns in thickness. For operation as a
detector the diode is reverse biased, where biasing refers to the application of an external
voltage across the p-n junction of the detector, to set up a depletion region across the full
thickness of the intrinsic material and therefore an electric field across the entire device.
When a charged particle or photon enters the material the silicon is ionized and free
electron hole pairs (charge carriers) are produced. The electric field causes the charge
carriers to separate, before they can recombine, and to drift towards the heavily doped
regions which form the external electrodes of the diode. The introduction of the charge
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carriers and their subsequent drifting induces a signal on the external electrodes which is
measured by external electronics.
The p-n junction is illustrated in Figure 1. The first two sub-figures show the material with
the fixed space charge illustrated as a sign inside a circle and the free charge carries as “”and “+” signs. The other sub-figures illustrate physical attributes of the junction.
Figure 1 : The p-n junction. 1) p and n-type material 2) p and n junction in thermal equilibrium 3) absolute
doping concentration 4) space charge density 5) free carrier density 6) electric field 7) electric potential
The electric field and the electrostatic potential inside an abrupt p+-n junction can be
calculated with the use of Poisson’s equation, given in Equation 1, where Neff is the
effective doping density of the semiconductor.
ρ
dE d 2V
q
=
=− =
N eff
2
dx dx
ε ε Si ε 0
Equation 1 : Poisson's equation
The boundary conditions are that the electric field and the potential are both equal to zero
at the edge of the space charge region, that is:
dV ( x = w)
=0
dx
and
V ( x = w) = 0
+
Equation 2 : boundary conditions for the abrupt p -n junction
Solving Poisson’s equation gives the potential as a function of distance inside the junction as
given by Equation 3. Inserting the device thickness gives the full depletion voltage of the
diode. The depletion width can also be expressed as a function of the applied potential, as
given in Equation 4.
3
V ( x) =
q
2ε Si ε 0
N eff x 2
+
Equation 3 : Potential inside the p -n abrupt junction as a function of distance
w(V ) =
2ε Si ε 0
q N eff
V
Equation 4 : The width of the depletion region as a function of the applied bias
Using the fact that the capacitance is given as the rate of change of the charge with
potential, the capacitance of the junction can be expressed as a function of the doping
density of depletion width as shown in Equation 5 to Equation 7.
C=
dQ dQ dw
=
⋅
dV dw dV
Equation 5 : The definition of capacitance as a function of depth inside the junction
C (V ) = A ⋅
ε 0 ε Si q N eff
2V
Equation 6 : The junction capacitance as a function of applied voltage
C ( w) =
ε Si ε 0 A
w
Equation 7: The capacitance as a function of depletion width
Device Types
Pad detector
The pad detector is a very basic silicon detector. It consists of a p+-i-n+ junction with one
large heavily doped pad on either face of the intrinsic bulk material. One face is p+ doped
and the other n+ doped. This means there is no way of deducing a particles interaction point
except to say that is occurred within the detector. The signal induced will depend on the
type of ionizing particle interacting in the detector and the depletion width (controlled by
the applied bias voltage).
Strip and Pixel Detectors
The detector’s highly doped regions (electrodes) can be segmented into strips or pixels.
When an ionising particle enters the detector ionising the material free electron-hole pairs
are created. These drift under the influence of the electric field in the depletion region and a
signal is induced on the electrodes in the vicinity of the original ionisation. This enables the
original position of the ionising particle to be reconstructed from the signal collected on the
segmented electrodes. Both types of detectors work in this way, the difference between
them being that the pixel detector has individual contacts that are arranged in a 2D array
while the strip device has only a 1D array of contacts. Therefore the pixel detector allows
the 2D determination of the original ionisation point rather than just the 1D. This laboratory
will only use strip devices.
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Energy deposition from a high energy particle
When a silicon detector is traversed by a high energy particle and only a small amount of
energy is lost by that particle per unit thickness of the detector, such a particle is known as a
Minimum Ionising Particle (a MIP). When the ionising particle passes a silicon atom in the
lattice it will tend to liberate a loosely bound valance electron. The electron will be ionised
at an excited energy state and via collision will enter thermal equilibrium with the lattice as
a free electron in the conduction band. Occasionally the ionising particle will interact with a
more tightly bound electron than the silicon valence electrons and as a result create an
electron with a significant amount of kinetic energy. This liberated electron can cause
secondary ionisation of the silicon. The energy deposited for this interaction is therefore
higher than that for the interaction with the valence electrons. As a result of these rare high
energy events the energy spectrum of the deposited energy in the silicon detector is a nonsymmetric distribution as shown in Figure 2. The distribution was first described by Landau
and therefore takes his name.
Most probable charge ≈ 0.7×
× mean
Mean charge
Figure 2 : The Landau distribution.
Due to the non-symmetric nature of the distribution the mean value is higher than the
most probable value. For a MIP the mean energy loss per unit length is 3.88 MeV
cm-1 or 116 keV for 300 µm of silicon; while the most probable energy loss is
approximately 0.7 times this or 81 keV for a 300 µm thick detector. In silicon it takes
3.6 eV to create an electron hole pair under ionisation by a MIP. Therefore the mean
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number of charge carriers created is 108 per micrometer and the most probable is
72 per micrometer. This results in a mean signal from a 300 µm thick detector of
32000 electrons or a most probable value of 22500 electrons; which is equal to 3.6
fC. It is the most probably energy that is used for calculations of collected charge
from a silicon detector.
Signal Generation and Ramo’s Theorem
The signal is induced on the external electrodes by moving charges and is observed as pulses
on an oscilloscope. Ramo’s theorem provides a way of calculating signals induced on the
electrodes of a detector by the movement of charge carriers. The drift field determines the
trajectory and velocity of the charge carriers. The induced signal on the electrode was first
formulated by Ramo (Published in Proc.IRE.27:584-585,1939) for a vacuum tube system,
and is valid for a semiconductor detector, using the Gauss identity. The theorem states that
the induced current is given by the dot product of the velocity vector vq and the weighting
field EW, as given in Equation 8. The weighting field is calculated for the condition that all the
electrodes in the system are held at zero potential except the collecting electrode which is
held at 1V.
r r
i = qEW ⋅ v q
Equation 8 : Ramo's theorem for the induced current
The induced charge is simply given as the integral of the induced current for the movement
of the charge carrier from point r0 to point r1, as given in Equation 9.
r1
Qa = ∫ ia dt = − q ∫ E w ⋅ dr
r0
Equation 9 : Ramo's theorem for the collected charge from the movement of a charge q from point r0 to r1.
The velocity of the charge carriers is given by Equation 10, where µ is the carrier mobility
and E the electric field.
v = µE
Equation 10 : Carrier drift velocity
The mobility is carrier type dependent and given below for silicon (subscript n for electrons,
p for holes).
µn = 1350cm2V-1s-1
µp = 480cm2V-1s-1
For a simple pad detector the electric field is given as V/d and the weighting field is given as
1/d where d is the separation of the electrodes (equal to the thickness of the device). As a
consequence the signal is generated equally for a given increment of travel for a charge
carrier throughout the thickness of the detector.
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In a strip or pixel detector the drift electric field is close to that of a pad device. However,
the weighting field is more complex as the 1V potential is applied only to the sense strip or
pixel. This gives a very non-uniform weighting field, as shown in Figure 3. As a consequence
the signal on the collecting electrode is dominated by the movement of the charge carriers
close to the collection electrode rather than equally throughout the bulk of the detector.
Figure 3 : Weighting potential for a strip (pixel) detector.
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Part 1: Pad Detector
Aim: Observe the change in the depletion depth versus bias voltage measured by the
capacitance voltage relationship and measure the charge collection versus bias voltage for
front and back side illumination.
Introduction: This part of the lab illustrates the operation of a reversed bias pn-junction as
detector for ionising particles. The first measurement shows how the depleted region
grows as a function of bias voltage, and the change in capacitance that it implies. The
second measurement shows how the signal size varies with bias voltage for front and back
side illumination.
Detector: The detector used in this part of the lab is a simple pad detector as shown in
Figure 4. The detector is a p-in-n diode which is 300µm thick with implants less than 2µm
thick. The front implant is heavily doped p-type, the bulk is n- and the back implant is n+. The
front side implant is covered with an aluminium layer to enable electrical contact to the
implant. This has a circular hole at the centre to facilitate light illumination. The back side is
covered by an aluminium grid for electrical contact and light illumination. The front pad is
surrounded by a p+ guard ring to reduce edge currents. The active area of the device is
approximately 0.5 x 0.5 cm2.
Figure 4 : The pad detector
Test box: A box containing the pad sensor mounted on a PCB with the necessary passive
components has been developed for this lab. The schematic of the circuit is shown in Figure
5. It has an LP filter at the input of the HV supply (R1, R2, C1) and AC coupling capacitors to
screen the LCR meter and the preamplifier from the high voltage (C2, C3). The box also
contains two LED used to generate signals in the pad detectors. They are mounted at the
top and bottom of the sensor, to make it possible to illuminate from both the back and the
front of the sensor. The wavelength of the light emitted from the LEDs is 624 nm, which
gives a penetration depth of approximately 10 μm. Figure 6 shows the content of the test
box.
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Figure 5: Schematic of the circuit in the black test box.
Figure 6: Photo showing the content of the test boxes.
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Measurements 1: Capacitance versus voltage characteristics
• Connect the box containing the pad detector and LEDs to the HV supply and the LCR
meter. The pulse generator and amplifier should not be connected for this part of
the lab. Configure the LCR meter to measure capacitance in parallel mode, at 10 kHz
frequency. To eliminate stray capacitance, the LCR meter has to be calibrated to
define the zero point. This is done by connecting one of the leads to the box and
leaving the other just connected to the meter. Switch on HV supply and the LCR
meter then press and hold the CAL button on the meter, OPn should appear in the
top right corner then press the same button again. The meter should now show a
value close to 0 pF.
•
Connect both LCR leads to the box; the reading will increase up to approximately
200pF. Now scan the bias voltage and measure the capacitance at each value.
Appropriate values for the scan is:
-2, -5, -10, -20, -30, -40, -50, -60, -80, -100, -120, -140, -160, -180,
-200, -220, -240, -260 and -280 V
NB: it will take a long time for the measurement value to settle at each point. To get
a consistent curve in a reasonable time, wait exactly the same time between
changing the voltage and reading the value, e.g. 20 seconds
•
Plot the capacitance against the voltage, then plot 1/C2 against V. Fit two straight
lines to the graph, one to the first sharp rise and the other to the plateau value. The
depletion voltage is conventionally defined as the intersection of these two lines
Question: Why should the applied voltage be negative?
Question: What is the role of the capacitor C3?
Question: Why does not the capacitance C3 influence the measurement?
Question: What is the depletion voltage of the device?
Results 1: The doping density Neff and the resistivity of the silicon can be determined from
the depletion voltage. By rearranging Equation 4, the depletion depth can be expressed as
2ε r ε 0V ( w)
. The depletion voltage is the voltage at witch the depletion depth is equal
w=
qN eff
to the thickness of the device (300µm). The resistivity can be calculated from the effective
1
doping concentration using ρ =
. This value in normally in the order of a few kΩcm.
qµ N N eff
Quantities:
ε Si = 11.9
ε 0 = 8.854 × 10 −14 ρ / cm
q = 1.602 × 10 −19 C
µ N = 1500 cm 2V −1 s −1
Question: What is the effective doping concentration?
Question: What it the bulk resistivity of the device?
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Measurements 2: Signal generated by front and back side illumination
• Disconnect the LCR meter and connect the amplifier to the output, make sure that
the power supply for the amplifier is on and at 10 V.
• Connect the pulse generator to connector labelled Front on the box. This will
generate short light pulses on the front-side of the detector. You can spy on signal
sent to the LED on Channel 1 on the oscilloscope.
• Look at the output of the pre-amplifier on channel 2 on the oscilloscope.
• Note immediately what you can see on the oscilloscope when there is 0V applied.
Scan the voltage from 0 to +2 V and observe the signal on the oscilloscope. Then
decrease from 0V to -100V, initially from 0 to -5V in 1V steps then from -10 to -150V
in steps of 10V. Note the signal height and peak position as a function of voltage.
Question: Why do you see a signal at 0 V?
Question: Explain the behaviour of the signal between 0 and +2 V
Question: Explain the variation of signal size versus bias voltage.
•
•
Reduce the bias voltage to 0 V and connect the pulse generator for back side
illumination and observe the signal on the oscilloscope.
Decrease the voltage from 0V to -150V in steps of 10V. Note the signal height and
peak position as a function of voltage.
Question: Why does the device behave so differently in front and back-side illumination?
Question: Why is the first observed signal delayed with back-side illumination?
Question: What is the depletion voltage of the device?
NB: The wavelength of the LED is 624 nm and light at this wavelength has a penetration
depth of approximately 10 μm in silicon.
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Part 2: Strip Detector
Aim
This section of the laboratory has several aims which are:
• To introduce the silicon detector module: consisting of the silicon detector, readout
chip and thermal and mechanical aspects.
• To observe the noise of a silicon strip detector as a function of bias voltage.
• To observe the signal spectra due to a minimum ionising particle in a silicon detector
and demonstrate the Landau distribution shape of collected charge.
• To observe the physical size of a charge cluster from a minimum ionising particle and
relate this to the position resolution of the detector.
Apparatus
The apparatus for this experiment consists of a silicon strip detector, a readout amplifier
chip, the readout system to control the readout chip, a Sr-90 beta electron source and a
Scintillator coupled to PMT for the trigger. The whole system is driven over the USB by a
laptop running Linux. There is a dedicated data acquisition code with a graphical user
interface, gui, and a root based data analysis code. Each item is described below and shown
in pictures at the end of the lab script.
The apparatus: The source and trigger
The experiment will attempt to characterise the silicon detector under the illumination of
minimum ionising particles, as found in a particle physics experiment. The experiment uses
a Strontium-90 source as the source of ionisation.
Strontium-90 source
Strontium-90 beta decays to Yttrium-90 which beta decays to Zirconium-90 which is stable.
End point energy of Sr-90 decay is 0.546MeV
End point energy of Y-90 decay is 2.28MeV
Sr-90 can be considered to be a pure electron emitter.
Figure 7 : Y-90 beta spectrum
The absorption of the beta electron is described by via the Bethe-Bloch process:
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The set-up
The electrons pass through the silicon and deposit all of their remaining energy in the
scintillator. The size of the signal from the scintillator/PMT is proportional to the electron
energy.
The signal from the PMT will trigger the readout of the silicon detector if the signal is over a
user set threshold value.
Look on the scope at the trigger signal from the PMT.
Adjust the threshold of the scope to decide on a good value for the threshold
- What happens if the threshold is too low?
13
-
What happens if the threshold is too high?
If the trigger threshold is set to trigger on low energy electrons, what problem in
the collected charge would happen (look at the Sr-90 beta spectra and the BetheBloch graph)?
The apparatus: The strip detector module
We have a 1cm long ATLAS prototype silicon strip detector with 100 strips. We have two
readout chips in our module with 128 channels each. Each channel is an independent
preamplifier with shaping to get full charge collection within 25ns.
The module has delicate wire bonds between the amplifier chip and the detector and
therefore the box containing the module will not be opened. Below is a picture of the
module that will be used in the experiment.
The understand the different aspects of a modern silicon detector module look at the SCT
module – our module is similar
- Identify the different parts of the module (silicon strips, amplifier chip, etc)
- Why are the chips and silicon detectors not next to each other?
- What is between the chip and detector?
- What does this represent electrically to the amplifier?
The strip detector is a p-i-n diode.
The strips can be either n+ or p+, see the diagram
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Figure 8 : A diagram of the silicon strip detector
Typical values of doping concentrations are:
n- & p- ~ 10-12 cm-3
n+ & p+ ~ 10-15 to 10-18 cm-3
-
For the two diagrams what is the sign of the bias voltage applied to the back side of
the detector required to reverse bias the detector?
What happens to the silicon material after heavy irradiation?
What effect does this have on the position of the p-n junction and how it grows with
bias voltage?
Look at the miniature ATLAS strip detector under the microscope.
- Identify the different parts of the detector
- Can you identify the bias resistor?
Noise
The total noise of the system measured as an equivalent noise charge at the input of the
amplifier, ENC, is given by:
2
2
2
2
ENC tot = ENC pa + ENC i + ENC RP + ENC RS
2
The individual noise sources are given in Error! Reference source not found. to Equation 14.
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ENC pa = A + B × C load
Equation 11 : ENC for the preamplifier, where A and B are pre-amplifier constants and Cload is the capacitive
load at the input of the pre-amplifier
ENC i =
e
q
qIτ
4
Equation 12 : ENC due to the detector leakage current for CR-RC shaping. τ is the shaping time of the shaper.
ENC RP =
e τkT
q 2 Rb
Equation 13 : ENC for the thermal noise from the parallel resistors. Rb is the bias resistor of the detector.
ENC RS = 0.395 × C load
Rs
τ
Equation 14 : ENC for the series resistance in the circuit. Rs is the resistance of the readout strip
Run the DAQ software and run a pedestal run.
Look at the pedestal and noise histograms.
- What is being plotted in each?
- What is the pedestal?
- Is the value constant
o across the two amplifiers?
o with time?
- Can you identify the two chips from the pedestal plot?
- What is the noise in the noise histogram?
- Can you identify where the detector is (100 strips)?
- Comment on the high and low points on the noise histogram
- Estimate what the average noise of the detector is?
Increase the bias voltage slowly from 0 to 20V
- What happens to the noise in the detector?
- What is changing with increasing bias and how does this change the noise, look at
equations above?
- Think about what could happen if the detector current was higher and increasing
with bias voltage.
Signal
The signal is induced on the external electrodes by moving charges (electrons and holes)
To find the signal in a given event, the signal collected on each given amplifier channel is
compared in turn to a cut, know as the seed cut. If the value is higher than the cut the
channel is considered to have signal.
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If the signal is higher than the seed cut, the output the neighbouring channel is looked at. A
second cut, the neighbour inclusion cut, is applied to this channel to include the signal on
the neighbour into the event (cluster). If the neighbouring channel is included the neighbour
inclusion cut is applied to its neighbouring channel. This process is repeated until the signal
on the neighbouring strip is below the inclusion cut.
The signal size is the sum of the signals on all the strips in the cluster.
The cut applied must reduce the likelihood of including noise, but should keep the signal.
Look at the following table of likelihood of an event being under a Gaussian function as a
function of the cut applied.
Table 1 : Probabbility of being inside a Gaussian distribution
-
Number of standard deviations
Probability of being under the curve
1
0.682689492137
2
0.954499736104
3
0.997300203937
4
0.999936657516
5
0.999999426697
6
0.999999998027
What do you think is a good value of the seed cut in units of standard deviations of
the noise Gaussian?
Explain why you have chosen this value
What do you think is a reasonable value for the neighbour inclusion cut?
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Data collection
Bias the detector to 120V (max of supply)
PMT is on (11V)
Align the Sr-90 source, Detector module and Scintillator
Run the DAQ software
(In the gui there should be three lines of text. If they are in red it means there has been a
problem somewhere and you should exit the program, switch the motherboard off at the
wall, unplug the USB cables and start again. If they are black then everything has been set
up correctly and is ready to run. )
Check that the trigger is set as you require (OR and Trigger in) and the threshold value as
you determined from the oscilloscope.
Log the Data to a file for the data from the detector (you need to decide upon a name)
Click on RS (radioactive source)
Click on Run
You should have a data rate of 7-9 Hz.
For the on-line analysis a seed cut of 5 noise sigmas is used.
Look at the different pull down menus
- What is being plotted in each window?
In the signal page look at the two chips individually
- What can you say about the signal in the two chips?
- Is the S/N cut good enough?
- What addition feature adds to the noise that you can remove in an off-line analysis?
In the signal page look at the time projection
- What is this showing you?
- Would this detector be any good for the LHC? (bunch crossing 25ns)
You should collect data for about 30 minutes.
If time is short then don’t worry there is an example data file for the off-line analysis.
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Data Analysis
Due to time constraints ask one of the lab demonstrators to edit the analysis code to include
your data file.
Run the analysis code
./analysis
Several windows (know as canvases) will appear
Look at the analysis canvas
What is plotted in the top left of the canvas?
- Why are chip 1 and chip 2 different?
- What does this say about your hit occupancy?
- If you were designing a detector system would you read out all the channels, if not
why not?
Look at the bottom left plot, data is only included in this plot if it passes the seed inclusion
and neighbour inclusion cuts.
- What is plotted?
- Comment on the shape of the plot
A timing cut is applied to the data to only include the data that is in the peak section (+/10ns around the peak). This data is called the peak data. Data is also plotted for a timing cut
of 80-100ns and called the baseline.
- What do you expect to be in the base line data plots?
Loot at the top right hand plot
- Do you see any noise events?
- How does the noise affect the signal?
- What can you say about the shape of the signal plot?
- Can you explain the physics of the shape?
- Is the reported peak at the same place as the apparent peak of the fit?
- If not do you think the fit is correct?
Look at the Henrycanvas (not Henrycanvas2)
- What is plotted?
- Comment on the shape?
- Why is the cluster width 2 highest? Do you think that this is reasonable?
- Why are there clusters more than 2 strips wide?
Look at Peak Distribution canvas
- What are you looking at?
- What can you say about the difference in the plots on the top left and bottom left?
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-
Do you think that this has a physical reason?
Thinking about the plots in the Henrycanvas and Peak distribution canvas
- Do you think the position resolution will be affected by larger clusters?
- If so will the affect be always good, always bad, or sometimes good and sometimes
bad? Explain why.
- Read the slides – “delta electrons and resolution in a silicon detector”
Conclusions
Look at both sets of results from each part of the experiment. Consider the different
attributes from each detector and the different ways in which they could be used.
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Pictures of the Strip laboratory set-up
Figure 9 : A sr-90 source aligned above the silicon detector with an aligned Scintillator trigger below.
Figure 10 : Motherboard with flat cable to daughter board, trigger input from PMT and USB cable connected.
Figure 11 : Example of the trigger signal on the oscilloscope.
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Appendix
The appendix has details on the hardware and the software. These details are not required
to perform the laboratory but should be of interest to the student.
The hardware
More details for the hardware are given here.
Strip detector
The strip detector is a more complex detector than the pad device. The strip layout means
the signal can be picked up on several electrodes and depending on the relative strength of
the signals the interaction position of the particle can to be determined. The silicon sensor
in this experiment is 1 cm x 1 cm and 300 µm thick. There are 100 strips at a pitch of 80 µm.
The strip detector has a silicon oxide layer between the implant and the aluminium readout
electrode. This oxide layer acts as a capacitor between the diode and the amplifier. Why do
you think this might be? To enable a D.C. connection across the diode a set of bias resistors
are used to connect each strip implant to a common implant (know as the bias rail or bias
ring). This common connection is connected to the H.V. return 9 at the ground potential of
the daughter board). The H.V. bias is supplied via a back side contact to the strip detector.
The detector is mounted on the daughter board and connects to a Beetle amplifier chips.
The two experiments use different types of strip detector. One has a p+-n--n+ diode and the
other an n+-p--p+ diode, where the first implant is the segmented strip structure.
Beetle Chip
The Beetle chip is an analogue readout chip. The daughter board contains two Beetle
readout chips. They each have 128 independent input channels of analogue amplifier and
shaper with a 25ns peaking time. The analogue signal from each channel is readout from
each chip as a multiplexed analogue signal. The input dynamic range of the amplifier is
around ±110000 electrons, that is to say about 5 MIPs of either positive or negative polarity.
Trigger, Pipeline
Triggered systems are used to identify what events should be stored for later analysis. As
only a limited amount of events can be stored the trigger is used to rapidly decide which
ones are interesting enough to keep. In the case of this lab the trigger will select events
where a particle passed through the sensor and deposits enough energy in the scintillator
for the output of the PMT to exceed a predefined value. Therefore in this set-up a
scintillator trigger is used.
The pipeline stores the events on the Beetle chip waiting for the trigger to decide whether
or not they are ‘interesting’.
Readout System
The readout system consists of the daughter board which supports the Beetle chips and the
mother board. The daughter board has an additional amplifier to amplify the signal for
transmission to the mother board. The mother board digitizes the data and transfers it via
the USB to the PC. The system contains two front-end readout chips. There are two
analogue outputs on the motherboard in order to probe the analogue output signal of each
Beetle chip before they are digitised. The mother board accepts an analogue signal from a
PMT to use as a trigger for data acquisition.
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The Software
There are two software codes; the data acquisition code to collect the data and perform online data analysis and the data analysis code to perform detailed off-line data analysis.
To start the DAQ system.
1. Start by opening a terminal window then type ‘ls’. This brings up the list of
directories. Make sure you see the directory Analysis_Compiled then type ‘cd
Analysis_Compiled’ to take you into that directory.
2. Plug the USB cable into the laptop and the motherboard, and plug in the
motherboard’s power supply. A green and red LED on the motherboard should
illuminate to show you it’s switched on.
3. In the terminal type ‘dmesg | grep tty’. The dmesg code checks amongst other
things the connections to the USB hub, and grep looks for the structure tty in the
codes output. The pipe (i.e. “|”) can be found by holding the “Alt Gr” button and the
button above tab. The last line of the code that appears should read “FTDI USB Serial
device convertor now attached to ttyUSB0”. The 0 could be replaced by any other
number and this is the number you use in the next line of code. We’ll just assume it’s
0 but make sure to change it if it’s any different.
4. Type ‘change_priv 0’ and press the blue reset button on the motherboard.
5. Type ‘alibava-gui – dev=/dev/ttyUSB0 lars.ini’. This will open the Alibava program
gui window. The input file lars.ini contains the set-up details for the Beetle chips.
•
On-line data analysis:
The graphs produced by this program are:
o Signal – used for the calibration and radioactive source runs. Only of interest for
the radioactive source run.
o Pedestals and noise – used for the pedestal and radioactive source runs. The
chip output is about 500 with no input signal. This varies from channel to channel
and event to event. The zero value is known as the pedestal. The noise on each
channel is calculated by calculating the standard deviation of the signal (pedestal
value) on that channel. The value is constantly updated. You will see on the
graph the distinction between the two chips on the daughterboard as only one is
connected to the detector so we will get a larger noise on one of them. Which
one is it? A large spike indicates a noisy channel and a low spike indicates an
unbonded channel. Both need to be masked out later. Also need to mask off 28
channels on the chip connected to the detector as there is no signal on them as
the detector only has 100 strips. The noise will change with boas voltage – which
way will it change and why?
o Hitmap – shows you the signal on the channel that is hit.
o Temperature – of the daughterboard
o Time structure – shows the time a trigger occurs, this should be uniform
o Event display – this doesn’t give the average, it displays the data on each channel
for just 1 event updated every 100 events.
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o Noise/Common mode – the noise graph gives the average noise for each chip as
a function of event number, the common noise is how the chip channels change
in a common fashion (for example they all jump up together). This is displayed as
a function of the event number, not channel number.
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Beta electron data analysis
The data analysis code allows you to reproduce the analysis performed by the acquisition
code and also apply your own cuts.
•
Edit the analysis code to upload your data files:
1. In the terminal window type ‘ls’ and check you can see the files you have just
created.
2. You will need to edit the code to tell it to use your data files. Type ‘nano
SingleRun.cpp’. Find in the code the section that is:
//================= HISTOGRAMS DISPLAYED ====================
sin_preguntas("RSPlanarD_ntype_Chip2_ID_150V_2nd_14122009.dat", 0,
//
"CalibrationPlanarD_ntype_Chip2_ID_150V_2nd_14122009.dat",
"PedestalsPlanarD_ntype_Chip2_ID_150V_2nd_14122009.dat",
1, 1, 7, 14, 40, 100);
// Not to use calibration -> input is sin_preguintas("RS",0,"Ped",polarity,
// fit,timecutlow,timecuthigh,baselinetimelow,baselinetimehigh)
//===============================================================
This shows you that code that is actually run is called sin_preguntas.cpp
It takes as its inputs
sin_preguintas("RS file", ”Calibration file, "Pedestal file", polarity, fit, timecutlow,
timecuthigh, baselinetimelow, baselinetimehigh)
3. Edit the line so that RS file, Calibration file, and Pedestal file correspond to the
files that you wish to analyse.
4. Where the polarity is the sign of the signal collected. This should be -1 if you are
collecting electrons and +1 if you are collecting holes. If you have a device with
+ve biased strips with respect to the back side what will be collected, holes or
electrons, what should the polarity be set to?
The fit value should be 1 to ask the code to perform the fits required.
Timecutlow and timecuthigh are cuts applied to the data to select only events that are on
the peak of the signal in the data analysis. This is described further down in more detail.
baselinetimelow, baselinetimehigh are cuts applied to the data to select events that are not
on the peak so that we can check that things look fine where there is no signal. The default
values for these should not need to be changed.
5. click ctrl and X to exit. It will ask if you’d like to modify buffer. Type yes.
• Editing of cuts used in the analysis:
As described above we will want to apply a cut to the data to include it as a signal so we
don’t include noise in the data. The signal seed cut is normally run at 6 times the noise
distribution standard deviation as described above. The signal to noise cut is to allow
neighbour inclusion and is set at a value of around 3 standard deviations of the noise. This
can be changed in sin_preguntis.cpp.
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To change the cuts:
1. in the terminal type “nano sin_preguntas.cpp”
2. type “ctrl W” for search
3. search for the string Seedcut
This finds the line:
A->set_cuts(6.,
4.);
//First:
Seedcut,
Second
Neighcut
FOR
RS
data
The first number is the seedcut, second is neighbour inclusion cut.
4. Change these to see the effect on the data.
5. click ctrl and X to exit. It will ask if you’d like to modify buffer. Type yes.
• Changing the range of the fit to the Landau spectrum:
You may also need to change fit range used on the final spectrum.
This should not normally be required. However, it might be of interest to see how the
returned value of the peak is affected by the choice of the fit range.
To change the fit range
1. in the terminal type “nano analysis.cpp”
2. ctrl W for search
3. search for the string // fit that we use
This finds :
hx->Fit("fFitLandNjaka", "wr", "", 20, 250); // fit that we use and
// the range over which it is performed.
4. Change 20 and 250 for lower and upper limits respectively to the number that you
desire.
5. click ctrl and X to exit. It will ask if you’d like to modify buffer. Type yes.
• Running the analysis code:
When ever you make any changes to the code you will need to recompile the code.
1. In the terminal type ‘make analysis’.
2. To run the code type ‘./analysis’. This opens the data files and the analysis program.
Several new windows (canvas) will appear and text will be output to the terminal screen.
The first canvas is the analysis canvas which should have 6 plots on it. Plots 1 and 2 show
the hits on chips 1 and 2 respectively. The time graph shows the signal size as a function of
the time between the trigger and the sampling time of the signal. This is in effect a
reconstruction of the shape of the signal out of the Beetle chips.
1. Note the peak of the plot. You will need to edit and saving the SignalRun.cpp file so
that the values for the time cuts are +/- 5 around this peak (This will make sure that
the data used in the analysis is data that is in time)
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2. Before editing and saving the SignalRun.cpp file examine the noise and common
mode noise plots. The noise and the common noise plots are the same as on the online analysis. You need to generate a masked file to mask out bad strips (noisy or not
connected) and the whole chip that isn’t connected to the detector. From the noise
plot note the strips that are not-bonded and that are noisy. The strip numbers can
be read off from the noise histogram whose scale can be edited by clicking on the
histogram.
3. Check masked_example.txt for an example of the mask file.
4. Edit the mask file using the nano code to include a list of all the channels to mask.
Save as masked.txt. The first chip has channels that run from 0 to 127 and the
second from 128 to 255.
5. Edit SignalRun.cpp so that the time cuts are correct.
Compile and re-run the data analysis code (make analysis, then ./analysis)
• Average detector noise:
The terminal window outputs some information on the analysis. At the end of this it states
the average recorded noise for chip 1 and 2.
• The output canvases:
The screens are the root code canvases for different plots from the results that were
recorded. Look at these in turn. The Peak clusters are those found for the in-time events and
the baseline are those found for the out of-time events. The baseline plots should be more
or less empty.
1. Analysis canvas is divided into 4 main sections. The top left shows on a log plot all
the data collected for each chip individually. The bottom left shows the average
signal size as a function of the time between the PMT trigger and the sample signal.
This is a reproduction of the signal time waveform. The top right plot is the pulse
height spectra after the signal to noise cuts and timing cuts have been applied. A
Landau convoluted with a Gaussian is fitted to the data. The final plot (bottom right)
is the noise plot showing the average noise per channel and a histogram of the
common mode noise contribution.
2. The distribution canvas has a graph which shows the size of the signal as a function
of the number of strips in the cluster. A black dot is draw for each event in the
analysis. Comment on the shape of the distribution.
3. The peak distribution canvas has four graphs. These are histograms of the signal
recorded on the detector for clusters that are only 1 strip wide, 2 strips and 3 or
more strips wide. The 4th graph of the total signal looks like the signal expected, the
total of all the strips put together. The distributions increase in value and have
higher energy tails as you get clusters which are more strips wide. Consider why this
happens.
4. The baseline distribution canvas is similar to the peak cluster but is for the out-oftime events and should look pretty empty.
5. The Imax canvas has a histograms which shows the number of events recorded for
each channel number; a nice collimated source should be visible.
6. The neighbour canvas has a histogram is like the peak distribution. It shows the
number of events as a function of deposited energy for the signals recorded on the
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neighbouring strips (left and right and second strip left and right). Comment on the
histograms.
7. The temperature canvas gives the temperature of the daughter board as a function
of time.
8. There are two Henry canvases one for the peak and one for the baseline. It is a
histogram of the cluster width. You can get rid of the bigger clusters by playing with
the noise inclusion cut.
To exit the program, hold ctrl and C.
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