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Register a new userAdvanced Methods for the Optical Quality Assurance of Silicon Sensors
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We describe a setup for optical quality assurance of silicon microstrip sensors. Pattern recognition algorithms were developed to analyze microscopic scans of the sensors for defects. It is shown that the software has a recognition and classification rate of $>$~90% for defects like scratches, shorts, broken metal lines etc. We have demonstrated that advanced image processing based on neural network techniques is able to further improve the recognition and defect classification rate.
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An analysis software was developed for the high aspect ratio optical scanning system in the Detec- tor Laboratory of the University of Helsinki and the Helsinki Institute of Physics. The system is used e.g. in the quality assurance of the GEM-TPC detectors being developed for the beam diagnostics system of the SuperFRS at future FAIR facility. The software was tested by analyzing five CERN standard GEM foils scanned with the optical scanning system. The measurement uncertainty of the diameter of the GEM holes and the pitch of the hole pattern was found to be 0.5 {mu}m and 0.3 {mu}m, respectively. The software design and the performance are discussed. The correlation between the GEM hole size distribution and the corresponding gain variation was studied by comparing them against a detailed gain mapping of a foil and a set of six lower precision control measurements. It can be seen that a qualitative estimation of the behavior of the local variation in gain across the GEM foil can be made based on the measured sizes of the outer and inner holes.
The Mu2e electromagnetic calorimeter has to provide precise information on energy, time and position for $sim$100 MeV electrons. It is composed of 1348 un-doped CsI crystals, each coupled to two large area Silicon Photomultipliers (SiPMs). A modular and custom SiPM layout consisting of a 3$times$2 array of 6$times$6 mm$^2$ UV-extended monolithic SiPMs has been developed to fulfill the Mu2e calorimeter requirements and a pre-production of 150 prototypes has been procured by three international firms (Hamamatsu, SensL and Advansid). A detailed quality assurance process has been carried out on this first batch of photosensors: the breakdown voltage, the gain, the quenching time, the dark current and the Photon Detection Efficiency (PDE) have been determined for each monolithic cell of each SiPMs array. One sample for each vendor has been exposed to a neutron fluency up to $sim$8.5~$times$~10$^{11}$ 1 MeV (Si) eq. n/cm$^{2}$ and a linear increase of the dark current up to tens of mA has been observed. Others 5 samples for each vendor have undergone an accelerated aging in order to verify a Mean Time To Failure (MTTF) higher than $sim$10$^{6}$ hours.
Optical inspection of 1191 silicon micro-strip sensors was performed using a custom made optical inspection setup, employing a machine-learning based approach for the defect analysis and subsequent quality assurance. Furthermore, metrological control of the sensors surface was performed. In this manuscript, we present the analysis of various sensor surface defects. Among these are implant breaks, p-stop breaks, aluminium strip opens, aluminium strip shorts, surface scratches, double metallization layer defects, passivation layer defects, bias resistor defects as well as dust particle identification. The defect detection was done using the application of Convolutional Deep Neural Networks (CDNNs). From this, defective strips and defect clusters were identified, as well as a 2D map of the defects using their geometrical positions on the sensor was performed. Based on the total number of defects found on the sensors surface, a method for the estimation of sensors overall quality grade and quality score was proposed.
The MAJORANA DEMONSTRATOR is an experiment constructed to search for neutrinoless double-beta decays in germanium-76 and to demonstrate the feasibility to deploy a large-scale experiment in a phased and modular fashion. It consists of two modular arrays of natural and $^{76}$Ge-enriched germanium detectors totalling 44.1 kg, located at the 4850 level of the Sanford Underground Research Facility in Lead, South Dakota, USA. Any neutrinoless double-beta decay search requires a thorough understanding of the background and the signal energy spectra. The various techniques employed to ensure the integrity of the measured spectra are discussed. Data collection is monitored with a thorough set of checks, and subsequent careful analysis is performed to qualify the data for higher level physics analysis. Instrumental background events are tagged for removal, and problematic channels are removed from consideration as necessary.
The event plane detector (EPD), installed in the Solenoid Tracker at the Relativistic Heavy-Ion Collider located at the Brookhaven National Laboratory is a plastic scintillator-based device that measures the reaction centrality and event plane in the forward region of the relativistic heavy-ion collisions. We used silicon photomultiplier (SiPM) arrays to detect the photons produced in the scintillator via the fiber connection. Signals from the SiPM arrays were amplified by the front-end electronic (FEE) board, and sent to the analog-to-digital converter (ADC) boards for further processing via the receiver(RX) board. The full EPD system consisted of 24 super-sectors (SSs); each SS was equipped with two SiPM boards, two FEE boards and two RX boards, and they corresponded to 744 readout channels. All these boards were mass produced at the University of Science and Technology of China, with a dedicated quality assurance (QA) procedures applied to identify any problems before deployment. This article describes the details of the QA method and the related test system. The QA test results are presented along with the discussions.
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