Poley, L., Blue, A., Bloch, I., Buttar, C., Fadeyev, V., Fernandez-Tejero, J., et al. (2019). Mapping the depleted area of silicon diodes using a micro-focused X-ray beam. J. Instrum., 14, P03024–14pp.
Abstract: For the Phase-II Upgrade of the ATLAS detector at CERN, the current ATLAS Inner Detector will be replaced with the ATLAS Inner Tracker (ITk). The ITk will be an all-silicon detector, consisting of a pixel tracker and a strip tracker. Sensors for the ITk strip tracker are required to have a low leakage current up to bias voltages of 500V to maintain a low noise and power dissipation. In order to minimise sensor leakage currents, particularly in the high-radiation environment inside the ATLAS detector, sensors are foreseen to be operated at low temperatures and to be manufactured from wafers with a high bulk resistivity of several k Omega.cm. Simulations showed the electric field inside sensors with high bulk resistivity to extend towards the sensor edge, which could lead to increased surface currents for narrow dicing edges. In order to map the electric field inside biased silicon sensors with high bulk resistivity, three diodes from ATLAS silicon strip sensor prototype wafers were studied with a monochromatic, micro-focused X-ray beam at the Diamond Light Source (Didcot, U.K.). For all devices under investigation, the electric field inside the diode was mapped and its dependence on the applied bias voltage was studied.
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Kuehn, S. et al, Bernabeu, J., Lacasta, C., Marco-Hernandez, R., Santoyo, D., Solaz, C., et al. (2017). Prototyping of hybrids and modules for the forward silicon strip tracking detector for the ATLAS Phase-II upgrade. J. Instrum., 12, P05015–26pp.
Abstract: For the High-Luminosity upgrade of the Large Hadron Collider an increased instantaneous luminosity of up to 7.5 . 10(34) cm(-2) s(-1), leading to a total integrated luminosity of up to 3000 fb(-1), is foreseen. The current silicon and transition radiation tracking detectors of the ATLAS experiment will be unable to cope with the increased track densities and radiation levels, and will need to be replaced. The new tracking detector will consist entirely of silicon pixel and strip detectors. In this paper, results on the development and tests of prototype components for the new silicon strip detector in the forward regions (end-caps) of the ATLAS detector are presented. Flex-printed readout boards with fast readout chips, referred to as hybrids, and silicon detector modules are investigated. The modules consist of a hybrid glued onto a silicon strip sensor. The channels on both are connected via wire-bonds for readout and powering. Measurements of important performance parameters and a comparison of two possible readout schemes are presented. In addition, the assembly procedure is described and recommendations for further prototyping are derived.
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Esperante-Pereira, D. (2014). DEPFET active pixel sensors for the vertex detector of the Belle-II experiment. J. Instrum., 9, C03004–11pp.
Abstract: Active pixels sensors based on the DEPFET technology will be used for the innermost vertex detector of the future Belle-II experiment. The increased luminosity of the e(+) e(-) SuperKEKB collider entails challenging detector requirements, namely: low material budget, low power consumption, high precision and efficiency, and a large readout rate. The DEPFET active pixel technology has shown to be a suitable solution for this purpose. A review of the different aspects of the detector design (sensors, readout ASICS and supplementary infrastructure) and the results of the latest thinned sensor prototypes (50 μm) are described.
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ATLAS Collaboration(Aad, G. et al), Cabrera Urban, S., Castillo Gimenez, V., Costa, M. J., Ferrer, A., Fiorini, L., et al. (2014). A neural network clustering algorithm for the ATLAS silicon pixel detector. J. Instrum., 9, P09009–34pp.
Abstract: A novel technique to identify and split clusters created by multiple charged particles in the ATLAS pixel detector using a set of artificial neural networks is presented. Such merged clusters are a common feature of tracks originating from highly energetic objects, such as jets. Neural networks are trained using Monte Carlo samples produced with a detailed detector simulation. This technique replaces the former clustering approach based on a connected component analysis and charge interpolation. The performance of the neural network splitting technique is quantified using data from proton-proton collisions at the LHC collected by the ATLAS detector in 2011 and from Monte Carlo simulations. This technique reduces the number of clusters shared between tracks in highly energetic jets by up to a factor of three. It also provides more precise position and error estimates of the clusters in both the transverse and longitudinal impact parameter resolution.
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