Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Optical Inspection of the Silicon Micro-strip Sensors for the CBM
  Experiment employing Artificial Intelligence   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 sensor's 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 sensor's surface, a method for the estimation of sensor's overall
quality grade and quality score was proposed.

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The categories of this paper are: hep-lat
12
The categories of this paper are: nucl-th
26
The categories of this paper are: bayes-an
17
The categories of this paper are: q-fin
14
The categories of this paper are: math-ph
24
The categories of this paper are: dg-ga
5
The categories of this paper are: gr-qc
30
The categories of this paper are: q-alg
9
The categories of this paper are: nlin
33
The categories of this paper are: plasm-ph
0
The categories of this paper are: hep-ph
32
The categories of this paper are: atom-ph
6
The categories of this paper are: astro-ph
28
The categories of this paper are: alg-geom
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The categories of this paper are: econ
7
The categories of this paper are: hep-th