\section{Sensor performance}
\label{sec:sensor}


\subsection{Configuration load}
The per-pixel five-bit configuration data were loaded and
read back to check for bit errors. Each load transfered
141\,kbits into the sensor configuration registers. The
load and readback cycle was repeated 25,000 times and
the data written and readback were compared. No bits
were seen to have been corrupted in this process. This
sets an upper limit on the bit error rate of $1\times 10^{-9}$
at 90\% confidence level.
%TO DO: DATA SPEED

\subsection{Pickup between pixels}

As mentioned in Section~\ref{sec:pixel:bulk}, pickup was
observed between pixels when large numbers fired at the
same time. For this reason,
all the results described in Section~\ref{sec:pixel} were
done with only the pixel(s) being studied being unmasked.
Typical pickup behaviour is illustrated in
figure~\ref{fig:sensor:pickup} where the threshold
scan for a single pixel is shown with just that pixel
unmasked and also with all pixels unmasked.
It is clear the distribution is uncorrelated with the
actual pedestal and depends on the pedestals of the
other pixels firing. The scan with pickup has a much
larger RMS and this has been used to characterise the
onset of pickup.
%
\begin{figure}[ht!]
\begin{center}
\includegraphics[width=6cm]{sensor/Run470422A.png}
\includegraphics[width=6cm]{sensor/Run470436A.png}
\caption{\sl 
Effect of pickup on a typical pixel with (left) the single
pixel enabled and (right) with all pixels enabled.
Note the difference in the x axis scale.
}
\label{fig:sensor:pickup}
\end{center}
\end{figure}

Many studies to analyse this effect were performed. It was found
that the level of pickup only depended on the number of pixels unmasked
but not their geometric positions relative to each other. One
example of these studies which shows this result clearly is shown
in figure~\ref{fig:sensor:masking}. In this study, a single pixel
on one edge of the sensor was unmasked. The RMS of this pixel
from a threshold scan was then determined as an increasing number
of pixels
were unmasked along the other edge of the sensor, i.e. at the
furthest point from the single pixel. It is seen that pickup on
isolated pixel has the same behaviour as that of all the pixels
along the far edge. Hence, the pickup is seen to be unrelated to
position.
%
\begin{figure}[ht!]
\begin{center}
\includegraphics[width=7cm]{sensor/pickupvsn.png}
\caption{\sl 
Onset of pickup, as measured by the RMS determined from a
threshold scan, as a function of the number of unmasked channels.
The x axis corresponds to the number of rows of 42 pixels
which were unmasked, in addition to a single edge row which
was always active.
The line labelled ``63,0'' corresponds to an isolated pixel
on one edge of the sensor,
while all the other lines correspond to a subset of pixels in
the row along
the opposite edge. It is seen that pickup starts around an
x axis value of 6, which corresponds to 294 pixels unmasked.
}
\label{fig:sensor:masking}
\end{center}
\end{figure}

It is thought that the pickup is caused by a power mesh
being shared between the pre-shape comparator and monostable
components. 
It is clear it is not coupling directly between
monostables as this would not be affected by masking pixels.
If a large number of pixels fire and so
give hits in the monostables, the common power level may
droop
and can cause the comparators in other pixels to
fire. 
The power meshes to these two components have been
separated in the next version of the sensor.


\subsection{Hit corruption}
The ``hit override'' mode described in
Section~\ref{sec:design:storage} allows hit data to be
stored in the sensor memory without requiring any
pixels to be above threshold. In this mode, all seven
banks of pixels in each row of 42 pixels will store
data for every timestamp and hence the 19 data registers
will be filled within the first three timestamps.

This test mode results in predictable values for the
MUX address, timestamp and row address for every data word
stored. These can be compared with the values read out
to check for corruption. (Note, the six-bit hit data 
themselves are not predictable as noise can cause them
to be randomly set.) A low level of corruption, at
around 0.1\%, was seen for the MUX address and timestamp
bits, although the actual rate varied from sensor to sensor.
This was traced to a design fault; the signal to write to
the SRAM did not have a wide enough voltage range to guarantee
the value would be written correctly. The rate of corruption
was reduced by applying an external voltage level of 2.7\,V
to increase the range and this was used for all the
results shown in this paper.
This design fault has been corrected in the next version
of the sensor.

\[TO DO: ADD TWINNING\]

%\subsection{Source response}
%Efficiency vs threshold for $^{90}$Sr.
%
%\begin{figure}[ht!]
%\begin{center}
%\includegraphics[width=6cm]{sensor/source.jpg}
%\caption{\sl Rate of hits vs threshold for $^{90}$Sr.}
%\label{fig:sensor:strontium}
%\end{center}
%\end{figure}


\subsection{Beam test}

Four sensors were exposed to an electron beam at DESY in
December 2007. This was relatively early in the sensor
testing and the sensors had not yet been fully understood.
In particular, the pedestal measurement and trim setting 
were not yet in use so
all sensors ran untrimmed. This required a much higher
threshold than would normally be used, due to the spread
of pedestals as shown in
figure~\ref{fig:pixel:pedsquad}, and hence a low
efficiency.

The sensors were mainly run in a ``tracker'' mode,
with no tungsten converter plates as would be required
for a calorimeter. This allowed charged tracks to be reconstructed
using the hits observed in each sensor. By connecting hits
in the outer two sensors, then the hits in the inner two
sensors could be compared with this interpolated track.
Figure~\ref{fig:sensor:beamresiduals} shows the 
distributions of the difference between the observed
hit and the interpolation for these two sensors,
following alignment. The residual widths are consistent
with that expected given the 50\,$\mu$m pixel pitch.
It is clear that hits due to
charged particles were observed.
%
\begin{figure}[ht!]
\begin{center}
\includegraphics[width=6cm]{sensor/residuals.jpg}
\caption{\sl 
Distribution of residuals for inner sensor hits
compared to outer sensor interpolated tracks. 
Note the y axis log scale.
}
\label{fig:sensor:beamresiduals}
\end{center}
\end{figure}


%\begin{figure}[ht!]
%\begin{center}
%\includegraphics[width=6cm]{sensor/beameff.jpg}
%\caption{\sl Efficiency vs threshold.}
%\label{fig:sensor:beamfakeeff}
%\end{center}
%\end{figure}

Following the beam test, the pedestals for each pixel were
measured and so the thresholds set in the beam test data
was corrected pixel-by-pixel to give the actual thresholds
used for each pixel. These actual thresholds had a wide
spread due to the uncorrected spread of the pedestals.
This in principle allows the hit efficiency to be found 
over this wide threshold range. However, in practise,
the overall low effiency meant that there were insufficient
track statistics available to extract an absolute efficiency
per pixel. Instead, a relative efficiency could be found
by comparing hits in neighbouring pixels for spatial
and time correlations. The absolute normalisation is
not known but assuming uniform exposure of all pixels
(which was true to a good approximation due to the
small size of the sensor) the relative rate in each pixel 
gives a relative efficiency measurement.
Figure~\ref{fig:sensor:beameff} shows the results of
this analysis. A strong fall-off of efficiency with
threshold is observed.
%
\begin{figure}[ht!]
\begin{center}
\includegraphics[width=6cm]{sensor/Sensor07Eff.png}
\caption{\sl 
Relative efficiency per pixel as a function of the
actual threshold for the pixel in TU.
}
\label{fig:sensor:beameff}
\end{center}
\end{figure}


\subsection{Cosmics}

\[TO DO: WAITING FOR STATISTICS\]

%Absolute MIPS efficiency
%
%\begin{figure}[ht!]
%\begin{center}
%\caption{\sl Efficiency vs real threshold.}
%\label{fig:sensor:cosmicseff}
%\end{center}
%\end{figure}
