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Hetero-epitaxial crystalline films underlie many electronic and optical technologies but are prone to forming defects at their hetero-interfaces. Atomic-scale defects such as threading dislocations that propagate into a film impede the flow…
A method for object classification that is based on distribution analysis is proposed. In addition, a method for finding relevant features and the unification of this algorithm with another classification algorithm is proposed. The…
This paper summarizes the introductory presentation for a workshop that explored the challenges of making precision astronomical measurements using deeply depleted (thick) CCDs. While thick CCDs provide definite advantages in terms of…
Atomic-scale defect detection is shown in scanning tunneling microscopy images of single crystal WSe2 using an ensemble of U-Net-like convolutional neural networks. Standard deep learning test metrics indicated good detection performance…
The growing complexity of particle detectors makes their construction and quality control a new challenge. We present studies that explore the use of deep learning-based computer vision techniques to perform quality checks of detector…
Point defects have a strong influence on the physical properties of materials, often dominating the electronic and optical behavior in semiconductors and insulators. The simulation and analysis of point defects is therefore crucial for…
Organic scintillators are widely used for fast neutron detection and spectroscopy. Several effects complicate the interpretation of results from detectors based upon these materials. First, fast neutrons will often leave a detector before…
Internal properties of a sample can be observed by medical imaging tools, such as ultrasound devices, magnetic resonance imaging (MRI) and optical coherence tomography (OCT) which are based on relying on changes in material density or…
There exists an enormous interest for the study of very high energy domain in particle physics, both theoretically and experimentally, in the aim to construct a general theory of the fundamental constituents of matter and of their…
Future complementary metal oxide semiconductor (CMOS) scaling for advanced integrated circuit (IC) technologies may well depend on "More than Moore" (MtM) approaches using heterogeneous integration of semiconductor-based devices. In order…
Two-dimensional (2D) materials have been extensively studied in recent years due to their unique properties and great potential for applications. Different types of structural defects could present in 2D materials and have strong influence…
Charge trapping in germanium detectors will inevitably impact their excellent spectral performance. Disordered regions in the germanium crystal structure, either created in the material during processing or induced by radiation exposure,…
With the thriving of deep learning, 3D Convolutional Neural Networks have become a popular choice in volumetric image analysis due to their impressive 3D contexts mining ability. However, the 3D convolutional kernels will introduce a…
Defects of Ge quantum dot arrays may affect the electrophysical, photoelectrical or optical properties of Ge/Si heterostructures as well as the functionality of devices produced on their basis. The defects of Ge quantum dot arrays formed at…
LaBr3:Ce crystals have good scintillation properties for X-ray spectroscopy. Initially, they were introduced for radiation imaging in medical physics with either a photomultiplier or SiPM readout, and they found extensive applications in…
Semiconducting two-dimensional (2D) transition metal dichalcogenides (TMDs) are considered a key materials class to scale microelectronics to the ultimate atomic level. The robust quantum properties in TMDs also enable new device concepts…
A long-standing limitation of semiconductor neutron detectors is the lack of a scalable solution to make large area instruments. Neutron detectors are used in a wide range of applications, including the nuclear industry, safeguarding…
In this research work, we have demonstrated the application of Mask-RCNN (Regional Convolutional Neural Network), a deep-learning algorithm for computer vision and specifically object detection, to semiconductor defect inspection domain.…
Flat-field images with thick, fully-depleted CCDs exhibit response variations near the edges of the chip and at other locations, such as the regoins bordering mid-frame blooming stop implants. Two possible origins for these repsonse…
In this paper we explore the effect of $\delta$-ray emission, fluctuations in th e signal deposition on the detection of charged particles in silicon-based detec tors. We show that these two effects ultimately limit the resolution that can…