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\newcommand{\ee}{\ensuremath{e^+e^-}\xspace}
\newcommand{\mq}{\ensuremath{\mu\text{m}^2}\xspace}
\newcommand{\mum}{\ensuremath{\mu\text{m}}\xspace}
\newcommand{\chisq}{\ensuremath{\chi^2}\xspace}

\journal{Nuclear Instruments and Methods A}

\begin{document}

\begin{frontmatter}

%% Title, authors and addresses

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\title{The SuperPix0 Small-Pitch Hybrid Pixel Detector with
Fast Sparsified Digital Readout: Beam Test Results}

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\author[pi]{S.~Bettarini}
\author[trento]{G.F.~Dalla Betta}
\author[to]{D.~Gamba}
\author[bo]{F.~Giorgi}
\author[normale]{A.~Lusiani}
\author[pi]{F.~Morsani}
\author[mi]{N.~Neri}
\author[pi]{E.~Paoloni}
\author[pi]{B.~Oberhof}
\author[pv]{L.~Ratti}
\author[bo]{C.~Sbarra\corref{cora}}
\author[bo]{M.~Villa}
\author[bo]{S.~Valentinetti}
\author[ts]{L.~Vitale}
\author[cracow]{M.~Chrz\k{a}szcz}

\address[pi]{Universit\'a degli Studi di Pisa and INFN-Pisa, L.go B. Pomtecorvo 3, 56127 Pisa, Italy}
\address[pv]{Universit\'a degli Studi di Pavia and INFN-Pavia, via Bassi 6, 27100 Pavia, Italy}
\address[bo]{Universit\'a degli Studi di Bologna and INFN-Bologna, via Irnerio 46, 40126, Bologna, Italy}
\address[ts]{Universit\'a degli Studi di Trieste and INFN-Trieste, Padriciano 99,34149 Trieste, Italy}
\address[mi]{Universit\'a degli Studi di Milano and INFN-Milano, via Celoria 16, 20133 Milano, Italy}
\address[to]{Universit\'a degli Studi di Torino and INFN-Torino, via Giuria 1, 10125 Torino, Italy}
\address[trento]{Universit\'a degli Studi di Trento and INFN-Padova, via Sommarive 14, 38123 Povo di Trento, Italy}
\address[normale]{Scuola Normale Superiore and INFN-Pisa, Piazza dei Cavalieri 7, 56126 Pisa, Italy}
\address[cracow]{Institute of Nuclear Physics of the Polish Academy of Science, Cracow, Poland}

\cortext[cora]{Corresponding author}


\begin{abstract}
%% Text of abstract
A prototype hybrid pixel detector with 50$\times$50~\mq\ pixels, 200~\mum\   thick sensor and sparsified
digital readout has been tested with a 120~GeV pion beam at the SPS H6 beam line at CERN.
Both effciency and resolution have been measured as a function of the discriminator threshold
and the angle of incidence of the impinging particles. The capabilities of the custom data-push readout
architecture have been tested as well. The viability of this technology
for the full-luminosity upgrade of the layer 0 of the SuperB vertex detector is discussed.
\end{abstract}

\begin{keyword}
CMOS pixels \sep Charged particle tracking \sep Hybrid pixel detector \sep Data-push readout
%% keywords here, in the form: keyword \sep keyword
%% MSC codes here, in the form: \MSC code \sep code
%% or \MSC[2008] code \sep code (2000 is the default)
\end{keyword}

\end{frontmatter}

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\section{Introduction}
\label{sec:intro}

The SuperB B-Factory \cite{SuperB}, a new concept asymmetric $\ee$ collider dedicated to heavy-flavour 
physics and expected to deliver unprecedented luminosities in excess of $10^{36}$~cm$^{-2}$s$^{-1}$, has
been funded by the Italian Ministry of Education, University and Research in the framework of the
2011-2013 National Research Plan (Dec. 24 2010). Its reduced center-of-mass boost with respect
to previous B-Factories (BaBar \cite{Babar} and Belle \cite{Belle}) 
asks for a factor two improvement on typical vertex resolutions to fully exploit
the accelerator potential for new-physics discoveries. In addition, the high luminosity and large
backgrounds expected at SuperB determine stringent requirements in terms of granularity, time resolution and radiation
hardness of all subdetectors and in particular, the vertex detector which is the closest to the interaction point.\\

The design of the SuperB Silicon Vertex Tracker
follows the model of the BaBar SVT \cite{SVT} but
comprises both an extended coverage and an additional innermost layer, called layer 0, located
at about 1.5~cm radius from the beam line. The layer 0 should offer a low material budget to minimize 
multiple scattering so as to meet the requirements on vertex resolution, and must be provided
with a high-speed readout to minimize the acquisition dead-time. 
Intense R\&D studies on various emerging technologies have been carried out to address further requirements such as
a small pitch to guarantee a hit resolution at the level of 10~\mum\ and to limit detector occupancy, the
capability to withstand background hit-rates up to a few tens of MHz/cm$^2$, large signal-to-noise ratio and 
low power dissipation. 
Standard high resistivity silicon detectors with short strips (striplets) will be used for the layer 0
during the first period of operation, when the luminosity will be gradually increased to reach the design
value. In fact, striplets offer a reasonably low material budget (about 0.2-0.3 \%$X_0$ for 200-300~\mum\ silicon
thickness) together with the required hit resolution. However, the detector occupancy becomes 
unaffordable at background rates larger than 5 MHz/cm$^2$ as expected at full luminosity, and a detector 
replacement is already scheduled after the first period of running.\\

This paper is focused on a prototype hybrid pixel detector named SuperPix0 and designed by
the VIPIX collaboration as a first iteration step aimed at the development of a device to be used for
the layer 0 upgrade.\\

Hybrid pixel devices are a well established technology in HEP experiments. The fully
depleted high-resistivity sensors and the read-out integrated circuits are built on different substrates
and then connected via high density bump-bondings. Hybrid pixel sensors usually provide high
signal-to-noise ratio, high radiation tolerance and 100\% fill factor. Furthermore, this technology
offers the possibility to implement advanced in-pixel electronics such as low-noise amplification, 
zero suppression and threshold tuning without the problem of cross-talk between the readout
logic and the sensor. The relatively large amount of material they are made of represents a 
disadvantage in terms of probability of particle scattering, although a reduction of material budget may
become possible with the latest technology improvements \cite{Bib5}. The main novelties of our approach
is the sensor pitch size (50$\times$50 \mq) and thickness (200~\mum)
as well as the custom front end
chip architecture providing a sparsified and data-driven readout. A prototype readout chip with
4096 cells arranged in a 32$\times$128 matrix was submitted for fabrication in standard 130~nm CMOS
technology by STMicroelectronics. The sensor was fabricated by FBK-IRST and interconnected
with the readout chip by IZM.\\

The paper is organized as follows: 

\section{The SuperPix0 Hybrid Detector}

\subsection{The High Resistivity Pixel Sensor}
\label{sec:sensor}
Pixel sensors are made from n-type, Float Zone, high-resistivity silicon wafers, with a thickness of 200~\mum\ 
and a nominal resistivity larger than 10 k$\Omega$cm. Sensors are of the ``n-on-n'' type and were fabricated at 
FBK (Trento, Italy) with a double-sided technology \cite{Zorzi}. N$^+$ pixels are arranged in a 2d array of 32$\times$128 elements 
with a pitch of 50~\mum\ in both X and Y directions, for a total active area size of 10.24~\mq\ . 
All around the pixels is a large n$^+$ guard ring extending up to the cut-line.  The electrical isolation between 
neighboring n$^+$ pixels has been obtained by means of a uniform p-spray implantation. 
A large p$^+$ diode is on the bias side: it has the same size as the active area and is surrounded by 6 floating rings. 
From electrical tests performed on wafers before bump-bonding (and connecting the sensors from the bias 
side only with a probe on the diode and a probe on the scribe line \cite{Huegging}), the total leakage current 
is about 1~nA, the depletion voltage is about 10~V, and the breakdown voltage in the order of 70~V, due to 
a relatively high p-spray dose. The pixel capacitance has also been estimated from measurements performed on a 
special test structure, and the resulting values are in the order of 
50~fF (i.e. close to the capacitance contribution expected from the bumps).
 
%Also sensors featuring a larger pixel array (210x128) have been tested, that are suitable for bump-bonding 
%to three identical read-out chips at a time. Due to the dead-area at the periphery of the read-out chips 
%and to the alignment tolerance, the pixel array has been oversized leaving some clearance (in the order of 1.5mm) at the edges.

\subsection{The Front End Cell}
\label{sec:cell}
The in-pixel analog electronics is made of a charge processor 
(shown in Fig.~\ref{fig:schematic}) where the sensor charge signal is amplified and compared to a chip-wide preset 
threshold by a discriminator. 
\begin{figure}[htbp]
\begin{center} 
\includegraphics[width=9cm]{schematic.pdf}
\caption{Block diagram of the analog front end electronics for the elementary cell of the SuperPix0 readout chip.}  
\label{fig:schematic} 
\end{center}
\end{figure}  
The in-pixel digital logic, which follows the comparator, stores the hit in an edge-triggered 
set reset flip-flop and notifies the periphery readout logic of the hit. 
The charge sensitive amplifier uses a single-ended folded cascode topology, which is a common choice for low-voltage, high gain amplifiers. 
The 20~fF MOS feedback capacitor is discharged by a constant current which can be externally adjusted, 
giving an output pulse shape that is dependent upon the input charge. The peaking time increases with the 
collected charge and is in the order of 100~ns for 16000~electrons injected. 
The charge collected in the detector pixel reaches the preamplifier input via the bump-bond connection. 
Alternatively, a calibration charge can be injected at the preamplifier input through a 10~fF internal injection 
capacitance so that threshold, noise and crosstalk measurements can be performed. 
The calibration voltage step is provided externally by a dedicated line. Channel selection is performed by means of a 
control section implemented in each pixel. This control block, which is a cell of a shift register, enables the injection 
of the charge through the calibration capacitance.  Each pixel features a digital mask used to isolate 
single noisy channels. This mask is implemented in the readout logic. 
The input device (whose dimensions were chosen based on~\cite{Ratti}) featuring an aspect ratio  W/L=18/0.3 and a drain 
current of about 0.5~$\mu$A, is biased in the weak inversion region. A non-minimum length has been chosen to avoid short channel effects. 
The PMOS current source in the input branch has been sized to have a smaller transconductance than the input transistor. 
For a detector capacitance of 100~fF, an equivalent noise charge of 150~e$^-$ rms was obtained from circuit simulations.  
The noise contribution arising from the leakage current can be neglected for the leakage current range considered in the 
simulations (0-2~pA). 2~pA~corresponds to ten times the anticipated leakage current for the pixel sensor. 
An overall input referred threshold dispersion of 350~e- rms was computed from Monte-Carlo simulations. 
Since SuperPix0 is the first iteration step aimed at the development of a readout chip for small pitch 
hybrid pixel sensors, in this design only the main functionalities have been integrated in the pixel cell. 
Threshold dispersion is a crucial characteristic to be considered in order to meet the required specifications 
in terms of noise occupancy and efficiency. Therefore, circuits for in-pixel threshold fine-adjusting have to be 
implemented in the next version of the chip.
The analog front end cell uses two power supplies. The analog supply (AVDD) is referenced to AGND, 
while the digital supply is referenced to DGND. Both supplies have a nominal operating value of 1.2~V. 
Since single-ended amplifiers are sensitive to voltage fluctuations on the supply lines, the charge 
preamplifier is connected to the AVDD. The threshold discriminator and voltage references are connected to 
both the AVDD and AGND. The in-pixel digital logic is connected to the digital supply. The 
substrate of the transistors is connected to a separate net and merged to the analog ground at the border of the matrix. 
The SuperPix0 chip has been fabricated in a six metal level technology. Two levels of metal have been used to route the analog signals, two for the digital ones and two for distributing the analog and digital supplies. The supply lines, at the same time, shield the analog signals from the digital activity.
For nominal bias conditions the power consumption is about 1.5~$\mu$W per channel. 
More details on the design of the analog front end chip can be found in the literature~\cite{Traversi}. 

\subsection{Digital Readout Architecture}
\label{sec:architecture}
The SuperPix0 digital readout architecture is an evolution of the one adopted for the APSEL4D
chip \cite{Slim5} and was originally designed to read out matrices of 320$\times$256 pixels and sustain rates of
100 MHz/cm$^2$ .
The same macro-pixel (MP) structure as described in \cite{Giorgi} has been adopted, but with a different
MP shape: 2$\times$8 pixel rectangles replace 4$\times$4 pixel squares in order to minimize the matrix mean
sweeping time (MST) in the presence of hit-clusters as expected in the
data. A further parallelisation level
is achieved by dividing the matrix in sub-matrices of 32$\times$64 pixels and providing each sub-matrix
with an independent readout and a local data buffer. A final output stage retrieves data from all the readout buffers and
compresses them into a single data stream. Hits are extracted from the matrix in a time-ordered
way, which was not the case with the APSEL4D chip and which allows
avoiding to add time information to each hit, thus reducing the total
amount of data to be transferred. 
Finally, a new hit encoding algorithm is used that includes a data
compression for clustered hits; in this way the output data band-width is
significantly reduced with a negligible increase of logic gates.\\

Each MP is connected to the peripheral readout through two private lines used to send a ``hit''
information when at least one of the pixels in the MP is fired and to receive a ``freeze'' signal to 
prevent all the pixels within the MP from accepting further hits until the readout has been completed.
The peripheral readout includes a time counter (BCO) which is incremented at the frequency of
a programmable clock defining the time resolution of the detector. Its value is used to provide a time-stamp
to each event. Whenever the BCO counter is
incremented, the MPs that have been hit during the previous time window are frozen and their hit-map 
is stored inside a FIFO together with the associated time stamp. The list of active MPs is then
used to extract hits from the matrix in a time-ordered way. A 32-bit wide pixel data bus is shared
by the rows and driven by the columns of the pixel matrix. For each BCO, the readout is performed
only on the columns in the corresponding MP list, one column per readout-clock cycle (down to
6 ns) independently of the pixel occupancy. Only pixels belonging to the fired MP are enabled to
drive the corresponding lines of the pixel data bus. When compared to a continuous sweep over the
matrix columns as performed with the APSEL4D chip, this technique slightly increases the mean
pixel dead-time. On the other hand, simulations demonstrated that the rectangular MP geometry
results in an overall improvement of performance with respect to the APSEL architecture for any
fixed number of hits.\\

A schematic of the perifery digital logic is shown in Fig \ref{fig:digitalReadout}. 
\begin{figure}
\centering
\includegraphics[width=3.0in]{./readoutSPX0.png} %
\caption{Schematics of the digital readout architecture.}
\label{fig:digitalReadout}
\end{figure}
As in the APSEL4D chip, the pixel
data are encoded by the sparsifier elements. They create a formatted list of all the hits found on
the pixel data bus and write it into a dedicated memory element called barrel. This component is
a FIFO memory with multiple write ports (one for each word in the list) and a conventional single
output port. A data concentrator controls the flux of data preserving the time-sorting of the hits.\\

Monte Carlo simulations have been performed on this architecture scaled to a 320$\times$256 matrix
in order to evaluate its performance. We measured efficiencies close to 98.5\% running with a 60
MHz readout-clock (200 MHz on the output bus) and starting from the
assumption of a 100 MHz/cm$^2$ hit rate. Whilst keeping
in mind that the target time resolution for this architecture is 1~$\mu$s, an efficiency drop is observed
with BCO lengths below 400 ns.


\subsection{Chip Characterization}
\label{sec:chip}

Five chip matrices have been characterized in terms of noise, threshold dispersion and gain
in various laboratory tests before the final trial on beam. The response of the sensors
was analysed as well. 
A photograph of one of the front end chips connected by bump-bonding to the high resistivity pixel sensor
matrix of 200~\mum\ thickness is shown in Fig.~\ref{fig:SPix0}. 
\begin{figure}
\centering
\includegraphics[width=3.0in]{./SuperPix0_bond.jpg} %
\caption{Photograph of the bump-bonded chip, the sensor matrix and the
  front end chip are 
visible as well as the bondings to the carrier.}
\label{fig:SPix0}
\end{figure}
The first laboratory checks identified a marginal problem in the
readout architecture that was investigated with dedicated studies. 
A particular data acquisition configuration allowed
the problem to be overcome, although the measurements were limited to 3\% of the pixels of the matrix for each run. 
Time constraints allowed the characterization of the front end electronics 
of about 10-20\% of the pixels in each matrix, depending on the chip.
The absolute calibration of the gain of the chip matrix was performed by using the internal calibration circuit
described in \ref{sec:cell}, which allowed the injection of charges from 0 to 12 fC in each preamplifier.
An average gain of 38~mV/fC was measured with a typical dispersion of about 6\% inside the examined
piece of  matrix. \\

Noise measurements and an evaluation of the threshold dispersion were
performed by measuring the hit rate 
as a function of the discriminator threshold. With a fit to the turn-on curve we obtain a pixel average equivalent 
noise charge (ENC) of about 77 $e^-$ with 15\% dispersion inside the matrix, and a threshold dispersion of 
about 500\ $e^-$, which motivated the project of
 a threshold tuning circuit at pixel level for the next submission.
Threshold dispersion, ENC and  gain values for each of the 5 chips 
characterized in the laboratory are reported in Table~\ref{tab:chip_charact}.
\begin{table}
\begin{center}
\begin{tabular}{c|ccc}
\hline
\hline
chip & thr. disp. ($e^-$) & ENC ($e^-$) & gain (mV/fC)\\
\hline %characterization in lab
12 & $460 \pm 30$ & $71 \pm 1$ &     37.3   \\
19 & $500 \pm 30$ & $85 \pm 1$ &     38.7   \\
53 & $520 \pm 30$ & $77 \pm 1$ &     38.6   \\
54 & $500 \pm 30$ & $77 \pm 1$ &     39.2   \\
55 & $580 \pm 30$ & $77 \pm 1$ &     36.9   \\
\hline
\hline
\end{tabular}
\caption{Lab characterization of the 5 chips tested during the test-beam.}
\label{tab:chip_charact}
\end{center}
\end{table}

Both beta ($^{90}$Sr) and gamma (Am) radioactive sources were used in order to test the sensor response and the
interconnections between the pixel electronics and the sensor. 
The hit rate as seen from the sensor matrix when exposed to $^{90}$Sr is shown in Fig..~\ref{fig:sr90}.
\begin{figure}[htb]
\centering
\includegraphics[width=3.2in]{./rate_Hz3.png}
\caption{Hit rate (Hz) measured with chip 19 exposed to a $^{90}$Sr source.}
\label{fig:sr90}
\end{figure}
The illumination of the matrix is not uniform due to the collimation of the source. The two blank columns
are due to a known problem in the front end chip. All tested chips showed a very good quality of the
 interconnections at 50~\mum\ pitch, as well as a responding sensor.
Only four channels out of more than  20 thousands showed interconnection problems.

\section{Beam Test Setup}
\label{sec:setup}
Due to beam time constraints, only three out of the five aforementioned SuperPix0 chips 
were tested with beam. The beam test was carried out at CERN, at the SPS H6
beam line delivering 120~GeV pions in spills lasting 9.5~s and separated by about 40~s. In
the region of the experimental setup the beam was characterized by widths of about 8 and 4~mm
rms on the horizontal and vertical planes, respectively. As a reference telescope six planes 
of 2$\times$2~cm$^2$, double-sided silicon strip detector with 25~\mum\ strip pitch on the p-side and 50~\mum\
pitch on the n-side \cite{Telescopio} were used. The readout pitch was 50~\mum\ on both sides. Three planes were placed
before the devices under test (DUT), and three after them, at distances of 3.5 cm from each other
and either 25 or 35 cm from the DUTs, depending on the configuration. All detectors were placed
on a custom motorized table with remote control. The reference telescope was used both to trigger
events and determine the impact point of tracks at the DUT. One of the
chips was used to study the dependence of the efficiency on the angle of the impinging particles, whereas either one
or two chips were put in the beam line when studying the dependence of the efficiency on the value of the
discriminator threshold. The schematics of both setups are shown in Fig \ref{fig:setup}.
\begin{figure}[htb]
\centering
\vspace{3cm}
\caption{Test beam setups with either one (left) or two (right) DUTs}
\label{fig:setup}
\end{figure}

\section{Trigger and Data Acquisition}
\label{sec:TDAQ}
The DAQ infrastructure is very similar to the one described in detail in \cite{Slim5}. The main
elements are two programmable VME 9U EDRO (Event Dispatch and Read-Out) boards \cite{Edro1,Edro2}
organized in a master-slave configuration and responsible for
programming the front end chips of
both the telescope and the DUT. The master EDRO is connected to the first, third, fourth and last plane of the
telescope. It generates and distributes to all elements both the readout and BCO clocks, as well as the
triggers. These are based on hit multiplicity on each side of the telescope planes connected to the
master. The slave is connected to the remaining planes of the telescope and to the DUT. Both EDRO boards
act as event builder, packing time-ordered information from the telescope
and the DUT in events that are then sent out via optical links (S-link \cite{Slink}) to a Robin card
\cite{Robin} on a remote PC where they are written to disk. Online monitoring is performed on another PC
complementing the DAQ system. The programmable BCO clock defines the time resolution of the
experiment by dividing the time in corresponding events. Its period
can vary from 400~ns up to 500~\mum\. 
For all data collected during the beam-test the BCO period was set to 5~\mum\. \\

The DAQ software is built on the ATLAS TDAQ software infrastructure \cite{TDAQ}, which provides a
complete environment with remote process control and communication, finite-state-machine, 
inter-process messaging, online monitoring and histogramming as well as a textual database infrastructure 
for run and front end configuration. The team developed applications, plugins, configuration and monitoring 
programs specific to our EDRO boards and information stored in the raw
events.\\

The analysis of the BCO information stored in each event allowed 
the DAQ rate to be measured, together with the DAQ dead-time, over the duration of each spill. 
A maximum acquisition rate in the order of 40~kHz was observed. The
data acquisition was dead-time free for the first half of each spill, when
events could be buffered in the Robin card while waiting to be
copied to disk. In the second half of each spill  the DAQ rate was limited to roughly 20~kHz. 

\section{Collected Data Sample}
\label{sec:data}

Trigger utilizzato; ulteriori richieste e loro efficienza dove applicabile;
statistica di tutti gli eventi utilizzati per le varie analisi/risultati e per chip (tabella?)

\section{Detector Performance}
\label{sec:analysis}

\subsection{Silicon Telescope Alignment and Track Reconstruction}

For each event, we reconstruct tracks using the silicon telescope
hits. We rely on the same track-reconstruction software that has been
used for the analysis of a former test-beam~\cite{Bettarini:2010zz}.
For this analysis, the track finding algorithm has been improved
to use a variable number of telescope planes, since we use now six
planes whereas four were used in the past.

Triggered events typically have just one track, with one
hit for each of the two sides, for each of the 6 telescope planes. We
set our reference frame with the $z$ axis along the beam, the $x$ axis
in the horizontal plane and the $y$ axis in the vertical plane,
pointing up. The $p$-side silicon strips measure the $u$ detector
coordinate along $x$, while the $n$-side strips measure the $v$
detector coordinate along $y$.
The reconstruction algorithm relies on the fact that the telescope
planes have high efficiency and low noise, and that most triggered
events contain just one track with all its related hits, with nothing
else but a very small number of noise hits. Adjacent fired strips are
grouped in clusters, and the position of each cluster is
calculated by weighting the strip positions with their measured
charge. The clusters primarily consist of one or two strips, in
similar proportions~\cite{Bettarini:2010zz}.
For each silicon detector, each $u$ hit is combined with each $v$ hit
on the other side to define a space hit. All possible straight lines
connecting the space-points of the two outer detectors are considered,
together with the closest space-points in the intermediate detectors.
The associated hits are fitted to straight lines with a minimum
$\chi^2$ fit, using roughly estimated hit uncertainties from the former
data analyses~\cite{Bettarini:2010zz}.

In a first phase, we align the telescope planes using only events
where a single track is reconstructed, with hits on all sides of all
planes within $1\,\text{mm}$ in the $xy$ projection, without any
requirement on the track \chisq. The beam tracks have an angular
distribution that is close to normal incidence
$5.0\pm0.2\,\text{mRad}$ in $xz$ plane, and $0.7\pm0.2\,\text{mRad}$
in the $yz$ plane. In these conditions, the data permit the alignment
of the $xy$ detector translations, and of the rotation angle around
the $z$ axis, whereas there is no significant sensitivity to align the
translations along $z$ and the rotations around $x$ and $y$ better
that the nominal position uncertainties.

We assume that the first and the last telescope planes are positioned at their
nominal position, and we align the remaining planes by minimizing the
residuals of the hits with respect to the extrapolated fitted tracks.
The alignment procedure is based on the measurements of the dependence of the
mean residual in the $u$ and $v$ views both from the $u$ and $v$
coordinates and is described in more detail
elsewhere~\cite{Bettarini:2010zz}.

After the telescope alignment, we select events with just one track,
with hits on both sides of all six planes, and $P(\chisq) \ge 10\%$.
In a typical run, all these requirements correspond to an efficiency
roughly around $50\%$ of all logged events. The resulting data-set has
residuals distributions (averaged over all telescope planes) which can be
approximately fitted with Gaussians with mean consistent with zero and
$\sigma = 5\,\mum$ and $\sigma = 9\,\mum$ in the $x$ and $y$ view,
respectively. The $p$-side resolution is better because the
presence of an additional floating strip improves the uniformity
of the charge splitting among the readout strips.

The residual distributions of a typical run
(Figure~\ref{fig:resid_fit_tele}) show systematic effects that induce
sizable deviations with respect to a Gaussian distribution. We could
not find evidence connecting the shapes of the residual distributions
to mis-bonded channels or to the presence of insensitive strips.  At
any rate, the widths of the residual distributions on the telescope
planes indicate that the extrapolation on the devices under test
($50\,\mum$-pitch hybrid pixels with digital readout) are precise
enough for the rest of the analysis.

\begin{figure}[tb]
\centering
\begin{overpic}[width=0.48\textwidth]{telescope-resid-u-r2665}
\put(25,77){(a)}
\end{overpic}
\begin{overpic}[width=0.48\textwidth]{telescope-resid-v-r2665}
\put(25,77){(b)}
\end{overpic}
\caption{%
  Example residual fit for track hits on the telescope silicon
  detectors, on the p-side (a) and on the n-side (b). The plots
  include the track fit hits residuals on all six planes combined.
  Because of the requirement on the track fit \chisq probability,
  the residual distribution tails are truncated.}
\label{fig:resid_fit_tele}
\end{figure}

\subsection{Hybrid Pixels Efficiency}

We study the efficiency of the device under test (DUT) as a function
of the angle of the track with respect to the normal to the detector
(incidence angle $\theta$), and as a function of the threshold used in the
digital comparator.  In different data-taking runs, the DUTs were
rotated in order to change the angle from $0^\circ$ to $70^\circ$ with
respect to normal incidence, in the $xz$ plane, and the thresholds
were varied from 730 to 820 DAC counts, corresponding to a range from
about 12.5\% to 40.6\% of a minimum ionizing particle
(m.i.p.). Relatively high thresholds were used in order to overcome
data-acquisition limitations of the prototype pixel detectors under
test. In the following, the plots include the result of a coarse
Monte Carlo simulation of the detector response, which is described in
the next section.

Hits are defined as clusters of fired pixels that are either adjacent
or separated by up to one non-fired pixel along either $u$ or $v$.
The cluster $u$ ($x$) and $v$ ($y$) positions are set to the
unweighted averages of the $u$ and $v$ positions of the fired pixels.

To associate DUT hits to the extrapolated track, we study the residual
distributions. We first align the DUT position in $x$ and $y$ by
measuring the mean of the $x$ and $y$ residuals. No alignment is
performed on the angle around the $z$ axis and on the other degrees of
freedom. After alignment, we observe centered and
approximately Gaussian residual distributions with a negligible amount
of noise hits~\ref{fig:resid_fit}.
\newif\ifFourResidFigs\FourResidFigsfalse
\begin{figure}[tb]
\centering
\begin{overpic}[width=0.48\textwidth]{det6-resid-u-r2665}
\put(35,77){(a)}
\end{overpic}
\ifFourResidFigs
\begin{overpic}[width=0.48\textwidth]{det6-resid-v-r2665}
\put(35,77){(b)}
\end{overpic}\\
\fi
\begin{overpic}[width=0.48\textwidth]{det6-resid-u-r2608}
\put(35,77){(b)}
\end{overpic}
\ifFourResidFigs
\begin{overpic}[width=0.48\textwidth]{det6-resid-v-r2608}
\put(35,77){(d)}
\end{overpic}
\fi
\caption{%
\ifFourResidFigs
  Example residual fit for tracks hitting a hybrid pixel
  detector at normal incidence, on the $u$ projection (a) and on the $v$
  projection (b), and at $60^\circ$ angle with respect to normal
  incidence, on $u$ (c) and $v$ (d).
\else
  Example $u$-view residual fit for tracks hitting a hybrid pixel
  detector at normal incidence (a) and at $60^\circ$ incidence angle
  (b).
\fi
  The residual is defined as
  the position of the reconstructed hit minus the extrapolated track
  position.  The curve shows a Gaussian fit.
  This data was taken at a threshold corresponding to about 25\% of a
  m.i.p.. The red vertical lines show the requirements on the residual
  to consider the hit associated to the extrapolated track.}
\label{fig:resid_fit}
\end{figure}
The width of the residual distributions is driven by the DUT intrinsic
resolution, which is nominally $50\,\mum/\sqrt{12} \approx 14.4\,\mum$
at normal incidence and increases with the incidence angle because the
track ionization affects a larger number of pixels, some of which may
more often be under threshold. From the geometry of $200\,\mum$ thick
sensors with $50\,\mum$ pitch pixels, we expect that tracks at
$60^\circ$ affect an average of 8 pixels along the $x$ coordinate,
augmenting the nominal expected intrinsic resolution to about
$400\,\mum/\sqrt{12} \approx
115\,\mum$. Figure~\ref{fig:resid_x_by_angle} reports the $x$ residual
width, as estimated by the $\sigma$ of a Gaussian fit, as a function
of the track incidence angle for the three pixel sensors under test.
\begin{figure}[tb]
\centering
\begin{overpic}[trim=0 0 0 30,width=0.48\textwidth,clip]{spix0_resid_width_x_angle_all}
\end{overpic}
\caption{%
Dependence of the $u$-view residual distribution width from the track
incidence angle for three pixel detectors under test. This data was
taken at a threshold corresponding to about 25\% of a m.i.p.. The plot
also reports the result of a Monte Carlo simulation.}
\label{fig:resid_x_by_angle}
\end{figure}
From $\theta = 0$ to $\theta = 70^\circ$, the width increases
approximately from $10\,\mum$ to $70\,\mum$, while the $y$ residual
width increases from $15\,\mum$ to $19\,\mum$. The discrepancy
between the $x$ and $y$ residual widths at normal incidence is
understood to originate from the different uncertainties of the track
extrapolation in the horizontal and vertical plane, caused by the
different resolution of the silicon telescope.

We associate hits to extrapolated tracks by requiring that they are
closer than 4 times the (angle dependent) residual distribution width
plus $60\,\mum$ to account for non-Gaussian tails caused by
delta-rays. We measure the efficiency by dividing the number of events
with at least one associated hit by the total number of tracks that
extrapolate to the sensitive area of the DUTs. Due to fabrication
defects, the prototypes are insensitive on 4 pixel columns at the
center of the wafers in the $u$-view: this area is excluded from the
sensitive area together with a safety margin corresponding to an
additional $50\,\mum$ pixel spacing.
To avoid border effects, we exclude from the sensitive area $50\,\mum$
from the top and the bottom and $150\,\mum$ from the left and the
right borders of the detector. We observe that the pixel detectors at
non-zero track incidence are inefficient in an area where their
aluminium frame intercepts the beam particles before they reach the
sensors. Although the extent of the effect along the $x$ coordinate is
well described by geometrical shadowing, the mechanism that causes the
observed inefficiency is not understood. As a consequence, the
inefficient area related to the shadowing effect is also excluded in
order to compute the efficiency. We obtain a Bayesan estimate of the
efficiency and its uncertainty using a Jeffreys'
prior~\cite{2009arXiv0908.0130C}, and we find that there is no
significant deviation with respect to using the naive estimators
$\epsilon = n/k$ and $\sigma^2(\epsilon) = n(n-k)/n^3$,
where $k$ denotes the number of hit-associated tracks and $n$ the
number of tracks that extrapolate to the sensitive area of the
sensors. Figure~\ref{fig:eff_by_angle} reports the efficiency as a
function of the track incidence angle for the three pixel sensors
under test, for data recorded with a threshold of 770 DAC counts,
corresponding to a signal of 1/4 of a m.i.p..
Figure~\ref{fig:eff_by_thr} reports the efficiency at normal
incidence as a function of the threshold, which has been varied
from 12.5\% to 40.6\% of the charge released by a m.i.p..
\begin{figure}[tb]
\centering
\begin{overpic}[trim=0 0 0 30,width=0.48\textwidth,clip]{spix0_eff_angle_all}
\end{overpic}
\caption{%
Hit efficiency as a function of the track incidence angle for three
pixel detectors under test. This data was taken at a threshold
corresponding to about 25\% of a m.i.p..  The plot also reports the
result of a Monte Carlo simulation.}
\label{fig:eff_by_angle}
\end{figure}
\begin{figure}[tb]
\centering
\begin{overpic}[trim=0 0 0 30,width=0.48\textwidth,clip]{spix0_eff_thr_all}
\end{overpic}
\caption{%
  Hit efficiency as a function of the threshold for normal-incidence
  tracks on three pixel detectors under test. The DAC counts
  correspond to a range from 12.5\% to 40.6\% of the charge released
  by a m.i.p..
  \iffalse
  The plot also reports the result of a Monte Carlo simulation.
  \fi
}
\label{fig:eff_by_thr}
\end{figure}

The inefficiency distribution is uniform across the $u$ and $v$
coordinates and no insensitive pixel was found. We have investigated
whether the inefficiency depends on the distance of the extrapolated
track from the center of the pixel, finding no evidence for such
hypothesis. The pixel sensors are close to full efficiency for
normal-incidence tracks at the reference threshold, which corresponds
to 25\% of a m.i.p., but this threshold setting is not robust because
we observe significant reductions of efficiency for non-zero track
incidence angles and, to a minor extent, for thresholds larger than
the reference one. When the track incidence angle increases, the
amount of traversed silicon increases as $1/\cos\theta$ but the charge
is split (along the $u$-view in this experimental setup) among a
larger number of pixels. The data indicate that there is a sizable
probability that all pixels affected by the track energy loss collect
a charge below the reference threshold.

\section{Hybrid Pixels Response Modeling}
\iffalse
Particle energy loss in material is described with a Landau
distribution with mean and width set as functions of energy according
to the literature~\cite{Bichsel:1988if}
\fi

We have modeled the response of the detectors under test with partial
success using a coarse Monte Carlo simulation, which includes the
energy loss in silicon with Landau fluctuations, the charge splitting
among the geometrically interested pixels, and the presence of a
threshold comparator. This simple model is unable to explain the
${\sim}0.5\%$ ``irreducible'' inefficiency at low thresholds and
normal incidence, which is understood as caused by the electronic
readout chain dead-time, but is able to model some of the data
results.

The simulation is most successful in modeling the efficiency
dependence on the incidence angle (Figure~\ref{fig:eff_by_angle}),
where it correctly simulates both the drop for angles $\theta >
15^\circ$ and the moderate rise from $60^\circ$ to $70^\circ$.  The
efficiency dependence derives from the combination of two effects: the
per-pixel collected charge decreases, but the number of affected
pixels increases. As a consequence, the probability that {\em all} the
pixels are under threshold first increases and then decreases.

The simulation does not appropriately simulate neither the efficiency
dependence of the efficiency from the threshold
(Figure~\ref{fig:eff_by_thr}) nor how the number of pixels above
threshold in a hit cluster varies with the track incidence angle
(Figure~\ref{fig:cluster_size_by_angle}). The simulation models
surprisingly well the increase of the $u$-view residual distribution
width with the angle (Figure~\ref{fig:resid_x_by_angle}).
\begin{figure}[tb]
\centering
\begin{overpic}[trim=0 0 0 30,width=0.48\textwidth,clip]{spix0_cluster_size_angle_all}
\end{overpic}
\caption{%
Hit cluster multiplicity as a function of the track incidence angle
for three pixel detectors under test. This data was taken at a
threshold corresponding to about 25\% of a m.i.p..  The plot also
reports the result of a Monte Carlo simulation.}
\label{fig:cluster_size_by_angle}
\end{figure}

\section{Conclusions}
\label{sec:conclusions}

The VIPIX collaboration has tested three prototype hybrid pixel
detectors with 120~GeV pions at the SPS H6 beam line at CERN.
A telescope consisting of six double-sided silicon strip detectors was
used to reconstruct the tracks that were used to evaluate the
performance of the sensors under test.
The efficiency has been measured as a function of the track incidence
angle and of the threshold used for the digital readout. Relatively
high threshold settings were used to overcome fabrication defects that
were affecting the readout chain.

From the width of the residual distributions, the detector resolution
appears to be consistent with the expectation for a $50\,\mum$ pitch
pixel detector with digital readout.

The pixel hit efficiency for normal incidence tracks and with a
threshold corresponding to the expected energy loss of 25\% of a
m.i.p. has been found to be about 99.5\%, apparently mainly limited by
the readout chain dead-time.  On the other hand, a significant efficiency
drop has been observed for incidence angles larger than $15^\circ$,
which is understood to be related to charge splitting among an increasing
number of pixels. A coarse Monte Carlo simulation is able to partially
model the features observed in the data and suggests that lowering the
threshold to ${\sim}13\%$ of a m.i.p. would result in a sensor that is
fully efficient at all incidence angles.

%% The Appendices part is started with the command \appendix;
%% appendix sections are then done as normal sections
%% \appendix

%% \section{}
%% \label{}

%% References
%%
%% Following citation commands can be used in the body text:
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%%   \cite{key}          ==>>  [#]
%%   \cite[chap. 2]{key} ==>>  [#, chap. 2]
%%   \citet{key}         ==>>  Author [#]

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\bibliographystyle{elsarticle-num}
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\bibliography{SuperPix0-bib}

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%% References without bibTeX database:

\begin{thebibliography}{99}

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