Transcript spectrum stabilization and relocation v

Multichannel Pulse Analysis
Chapter No. 18
Radiation Detection and Measurements,
Glenn T. Knoll,
Third edition (2000), John Willey .
Ch 18 GK
I  SINGLE·CHANNEL METHODS
II  GENERAL MULTICHANNEL CHARACTERISTICS
 Number of Channels required
 Calibration and linearity
III
THE MULTICHANNEL ANALYZER
Basic components and functions
 Analogue to digital converter
 Linear Ramp Converter (Wilkinson type)
 The Successive Approximation ADC
 Sliding scale Principle
 The Memory
 Ancillory Functions
 Multiscaling
 Multiparameter Analysis
 MCA dead time
IV
V
 SPECTRUM STABILIZATION AND RELOCATION
 SPECTRUM ANALYSIS
2
18-11:The basic function of the MCA involves only the ADC and the
memory. For purposes of illustration, we imagine the memory to be
arranged as a vertical stack of able locations, ranging from the first
address (or channel number 1) at the bottom the maximum location number
(say, 1024) at the top. Once a pulse has been processed by the ADC, the
analyzer control circuits seek out the memory location corresponding to
digitized amplitude stored in the address register, and the content of that
location is
incremented by one count. The net effect of this operation can be thought
of as one in which pulse to be analyzed passes through the ADC and is
sorted into a memory location corresponds most closely to its amplitude.
This function is identical to that described for the stacked single-channel
analyzers illustrated in Fig. 18.1. Neglecting dead time, input pulse
increments an appropriate memory location by one count, and therefore
total accumulated number of counts over all memory is simply the total
number of presented to the analyzer during the measurement period. A plot
of the content of channel versus the channel number will be the same
representation of the differential height distribution of the input pulses as
discussed earlier for the stacked single-channel- analyzers.
A number of other functions are normally found in an MCA.As illustrated in
Fig.18.7, an input gate is usually provided to block pulses from reaching the
ADC during the time it} is "busy" digitizing a previous pulse. The ADC
provides a logic signal level that holds the input gate open during the time
it is not occupied. Because the ADC can be relatively slow, high counting
18-12: To help remedy this problem, most MCAs provide an internal clock
whose output pulses are routed through the same input gate and are stored
in a special memory location. The clock output is a train of regular pulses
synchronized with an internal crystal oscillator. If the fraction of time the
analyzer is dead is not excessively high, then it can be argued that the
fraction'of clock pulses that is lost by being blocked by the input gate is
the same as the fraction of signal pulses blocked by the same input gate.
Therefore, the number of clock pulses accumulated is a measure of the live
time of the analyzer or the time over which the input gate was held open.
Absolute measurements can therefore be based on a fixed value of live
time, which eliminates the need for an explicit dead time correction to the
data. Further discussion of the dead time correction problem for MCAs is
given later in this chapter.
Many MCAs are also provided with another linear gate that is controlled by
a single channel analyzer. The input pulses are presented in parallel to the
SCA and, after passing through a small fixed delay, to the linear pulse input
of this gate. If the input pulse meets the amplitude criteria set by the
SCA, the gate is opened and the pulse is passed on to the remainder of the
MCA circuitry. The purpose of this step is to allow rejection of input pulses
that are either smaller or larger than the region of interest set by the SCA
limits. These limits, often referred to as the LLD (lower-level discriminator)
and ULD (upper level discriminator), are chosen to exclude very small noise
pulses at the lower end and very large pulses beyond the range of interest
at the upper end. Thus, these uninteresting pulses never reach the ADC and
18-13: If an MCA is operated at relatively high fractional dead time (say,
greater than 30 or 40%), distortions in the spectrum can arise because of
the greater probability of input pulses that arrive at the input gate just at
the time it is either opening or closing. It is therefore often advisable to
reduce the counting rate presented to the input gate as much as possible by
excluding noise and insignificant small-amplitude events with the LLD, and if
significant numbers of large-ampliude background events are present,
excluding them with an appropriate ULD setting.
The contents of the memory after a measurement can be displayed or
recorded in a number of ways. Virtually all MCAs provide a CRT display of
the content of each channel as the Y displacement versus the channel
number as the X displacement. This display is therefore a graphical
representation of the pulse height spectrum discussed earlier. The display
can be either On a linear vertical scale or, more commonly, as a logarithmic
scale to show detail over a wider range of channel content. Standard
recording devices for digital data, including printers and storage media, are
commonly available to store permanently the memory content and to provide
hard copy output.
Because of the similarity of many of the MCA components just described to
those of the standard personal computer (PC), there is a widespread
availability of plug-in cards that will convert a PC into an MCA. The card
must provide the components that are unique to the MCA (such as the ADC),
but the normal PC memory, display, and I/O hardware can be used directly.
Control of the MCA functions is then provided in the form of software
18-14: Some compromises in performance of the plug-in boards are
often necessary because the noisy electronic environment inside the
PC produced by the many digital switching operations is somewhat
hostile to the sensitive analog operations required in ADCs. Thus
there are also units in which the ADC operations are housed within an
external NIM module that communicates with the PC through an
interface cable. In some cases, the MCA is incorporated in a
computer-based spectroscopy system
that allows software control of the MCA functions as well as other
settings such as detector voltage supply and parameters of the
shaping amplifier such as gain, shaping time, pile-up rejection, and
spectrum stabilizer operation (see later section in this chapter).
B. The Analog-to-Digital Converter1. GENERAL SPECIFICATIONS
The job to be performed by the ADC is to derive a digital number that is
proportional to the amplitude of the pulse presented at its input. Its
performance can be characterized by several parameters:
1. The speed with which the conversion is carried out.
2. The linearity of the conversion, or the faithfulness to which the digital
output is proportional to the input amplitude.
3. The resolution of the conversion. or the "fineness" of the digital scale
corresponding to the maximum range of amplitudes that can be converted.
The nominal value of the resolution depends on the number of bits provided
by the ADC, and is specified as the maximum number of addressable
18-15:For the types of ADCs generally chosen for use in MCAs, the
effective resolution should not deviate greatly from the nominal value. The
voltage that corresponds to full scale is arbitrary, but most ADCs nuclear
pulse spectroscopy will be compatible with the output of typical linear Zero
to 10 V is thus a common input span. Shaping requirements will also usually
be specified for the input pulses, and most ADCs require a minimum pulse
width of a of a microsecond to function properly.
The conversion gain of an ADC specifies the number of channels over
amplitude range will be spread. For example, at a conversion gain of 2048
channels, 0 to lO-V ADC will store a 10 V pulse in channel 2048, whereas
at a conversion gain that same pulse would be stored in channel 512. At the
lower conversion gain, a fraction of the MCA memory can be accessed at
anyone time. On many ADCs, the conversion gain can be varied for the
purposes of a specific application. The resolution of the ADC must be at
least as good as the largest conversion gain at which it will be used.
The conversion speed or dead time of the ADC is the critical factor in
determining overall dead time of the MCA. Therefore, a premium is placed
on fast conversion, but practical limitations restrict the designer in speeding
up the conversion before linearity to suffer. The fastest ADCs, the flash or
subranging type discussed in Chapter 17, are rarely used in MCAs because
of their poor differential linearity. Two other types dominate in temporary
MCAs: linear ramp converters and successive approximation ADCs.
The first these, although the slowest, generally has the best linearity and
2. THE UNEAR RAMP CONVERTER (WILKINSON TYPE)-18-16
The linear ramp converter is based on an original design by Wilkinson and is
illustrated in Fig. 18.8. The input pulse is supplied to a comparator circuit
that continuously compares the amplitude with that of a linearly increasing
ramp voltage. The ramp is conventionally generated by charging a capacitor
with a constant-current source that is started at the time the
input pulse is presented to the circuit. The comparator circuit provides as its
output a gate pulse that begins at the same time the linear ramp is initiated.
The gate pulse is maintained "on" until the comparator senses that the linear
ramp has reached the amplitude of the input pulse. The gate pulse produced
is therefore of variable length, which is directly proportional
to the amplitude of the input pulse. This gate pulse is then used to operate a
linear gate that receives periodic pulses from a constant-frequency clock as
its input. A discrete number of these periodic pulses pass through the gate
during the period it is open and are counted by the address register. Because
the gate is opened for a period of time proportional to the input pulse
amplitude, the number of pulses accumulated in the address
register is also proportional to the input amplitude. The desired conversion
between the analog amplitude and a digital equivalent has therefore been
carried out. Because the clock operates at a constant frequency, the time
required by a Wilkinson type ADC to perform the conversion is directly
proportional to the number of pulses accumulated in the address register.
Therefore, under equivalent conditions, the conversion time for large pulses is
3. THE SUCCESSIVE APPROXIMATION ADC-18-17
The second type of ADC in common use is based on the principle of
successive approximation. Its function can be illustrated by the series of
logic operations shown in Fig. 18.9. In the first stage, a comparator is used
to determine whether the input pulse amplitude lies in the upper or lower
half of the full ADC range. If it lies in the lower half, a zero is entered
in the first (most significant) bit of the binary word that represents the
output of the ADC.
If the amplitude lies in the upper half of the range, the circuit effectively
subtracts a value equal to one-half the ADC range from the pulse amplitude,
passes the remainder on to the second stage, and enters a one in the most
significant bit. The second stage then makes a similar comparison, but only
over half the range of the ADC. Again, a zero entered in the next bit of
the output word depending on the size of the remainder passed from the
first stage. The remainder from the second stage is then passed to the
and so on. If 10 such stages are provided, a 10-bit word will be produced
that will cover a range of 210 or 1024 channels.
In its most common circuit implementation, the successive approximation ADC
multiple use of a single comparator that has two inputs: one is the sampled
and held voltage and the other is produced by a digital-ta-analog converter
(DAC). For the stage comparison, the input to the DAC is set to a digital
value that is half the put range. Depending on the result of the initial
comparison, the second-stage is then carried out with the digital input to
the DAC set to either 25 or 75% of the and so on. In this way, analog
4. THE SLIDING SCALE PRINCIPLE-18-18
The linearity and channel width uniformity of any type of ADC can be
improved by employing a technique generally called the sliding scale or
randomizing method. Originally suggested in 1963 by Gatti and co-workers,8
the method has gained popularity (e.g., Refs. 9 and 10) through its
implementation using modern IC technology. It has been particularly
helpful in improving the performance of both successive approximation and
flash ADCs. Without the technique, pulses of a given amplitude range are
always converted to a fixed channel number. If that channel is unusually
narrow or wide, then the differential linearity will suffer in proportion to
the deviation from the average channel width. The sliding scale principle is
illustrated in Fig. 18.10. It takes advantage of the averaging effect
gained by spreading the same pulses over many channels. A randomly chosen
analog voltage is added to the pulse amplitude before conversion and its
digital equivalent subtracted after the conversion. The net digital output is
therefore the same as if the voltage had not Digital output been added.
However, the conversion has actually taken place at a random point along
the conversion scale. If the added voltage covers a span of M channels,
then the effective channel uniformity will improve as \1M if the channel
width fluctuations are random. The implementation of Fig. 18.10 derives the
added voltage by first generating a random digital number and converting
this number to an analog voltage in a DAC. The same digital
number is then subtracted after the conversion
18-19: One of the disadvantages of the technique is that the original ADC
scale of-H"C1iannels is reduced to N - M. If a pulse occurs that would
normally be stored in a channel number near the top of the range, the
addition of the random voltage may send the sum off scale. Other design
also provoid this limitation by using either upward averaging (as described
above) or downward averaging (by subtracting the random voltage) depending
on whether the original pulse lies in the lower or upper half of the range.
The choice of M can then be as large as N /2 to maximize the averaging
effect without reducing the effective ADCscale. Because the sliding scale
method involves converting a fixed pulse amplitude through different
channels whose width may vary, a potential disadvantage is a broadening of
typical channel profiles. If this broadening is severe enough, it will
compromise the resolution of the ADC. Another potential problem is that, if
the addition and subtraction steps are not perfectly matched in scale
factor, periodic structures can be generated in the differential
nonlinearity that appear as artifacts in recorded spectra.
C. The Memory
The memory section of an MCA provides one addressable location for every
channel. Any of the standard types of digital memory can be used, but
there is sometimes a preference for "nonvolatile" memory, which does not
require the continual application of electrical. power to maintain its content.
Then, data acquired over long measurement periods will not be lost if the
power to the MCA is accidentally interrupted. Most MCAs make provisions
for subdividing the memory into smaller units for independent acquisition and
I:Ancillary Functions 18-20.
1. MEASUREMENT PERIOD TIMING
Virtually all MCAs are provided with logic circuitry to terminate the analysis
period after a predetermined number of clock pulses have been accumulated.
One often has the choice between preset live time or clock time, which are
distinguished by whether the clock pulses are routed through the input gate
(see Fig. 18.7). Normally, quantitative comparisons or subtraction of
background are done for equal live time periods, and this is the usual way of
terminating the analysis period.
2. MULTISCALING
Multichannel analyzers can be operated in a mode quite different from pulse
height analysis, in which each memory location is treated as an independent
counter. In this multiscaling mode, all pulses that enter the analyzer are
counted, regardless of amplitude. Those that arrive at the start of the
analysis period are stored in the first channel. After a time known as the
dwell time, the analyzer skips to the second channel and pulses of all
amplitudes at that memory location. Each channel is sequentially. One such
dwell time for accumulating counts, until the entire memory has The dwell
time can be set by the user, often from a range as broad as from 1 f.lS to
minutes. The net effect of this mode is to provide a number of independent
to the number of channels in the analyzer, each of which records the total
number of Over a sequential interval of time. This mode of operation can be
very useful in the behavior of rapidly decaying radioactive sources or in
3. COMPUTER INTERFACING-18-21
Stand-alone MCAs share many features with general purpose computers. In
its most I' form, the MCA can only increment and display the memory, but
more elaborate operatic"~ can be carried out if it is provided with some of
the features of a small computer. ", example, one of the most useful
functions is to allow summation of selected portions of.:“ spectrum,
generally called regions of interest (ROIs). Cursors are generated whose
position ~."‘ the displayed spectrum can be used to define the upper and
lower bounds of the channel n~;bers between which the summation is
carried out. This operation has obvious practical use fof;; simple peak area
determination in radiation spectroscopy. Other operations, such as
additiori~
or subtraction of two spectra or other manipulations of the data can also
be provided. .More complex computer-based systems are also widely
available that are based on p~~ with an appropriate ADC under software
control. In this approach, the functions mentioned above can be duplicated
through software routines that may be modified or supplemented
by the user. Useful operations of this type can range from simple
smoothing of the spectra to damp out statistical fluctuations, to elaborate
spectrum analysis programs in which the position and area of apparent
peaks in the spectrum are identified and measured. Current manufacturer's
specification sheets are often the best source of detailed information in
this rapidly eVOlving area.
4. MULT/PARAMETER ANALYSIS-18-22
The simplest application of multichannel analysis is to determine the pulse
height spectrum of a given source. This process can be thought of as
recording the distribution of events over a single dimension-pulse amplitude.
In many types of radiation measurements, additional experimental
parameters for each event are of interest, and it is sometimes desirable to
record simultaneously the distribution over two or more dimensions.
One example is in the case in which not only the amplitude of the pulse
carries information, but also its rise time or shape. Categories of events
can often be identified based on unique combinations of amplitUde and
shape, while a clean separation might not be possible using either parameter
alone. In the example shown in Fig. 18.11, both the amplitude and
shape (measured from the rise time) of each pulse from a liquid scintillation
counter are derived in separate parallel branches of the pulse processing
system. The object will now be to store this event according to the
measured values of both these parameters. In any unit designed for
multiparameter analysis, at least two separate inputs with dedicated
ADCs must be provided, together with an associated coincidence circuit. The
memory now consists of a two-dimensional array in which one axis
corresponds to pulse amplitude and the other to pulse shape. Because both
parameters are derived from the same event, they appear at the two inputs
in time coincidence. The multiparameter analyzer recognizes
the coincidence between the inputs and increments the memory location
corresponding to the intersection of the corresponding pair of digitized