Search research articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Related Concept Videos

Biasing of Metal-Semiconductor Junctions

Biasing of Metal-Semiconductor Junctions

Biasing metal-semiconductor junctions involves applying a voltage across the junction. Specifically, the metal is connected to a voltage source, while the semiconductor is grounded. This technique is essential for controlling the direction and magnitude of current flow in electronic devices, including diodes, transistors, and photovoltaic cells.
In Schottky junctions, where the semiconductor is n-type, applying a positive voltage to the metal relative to the semiconductor reduces its Fermi...

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by

Same author

Study of Dangling Bond States in Magnetron-Sputtered a-Si Thin Films via Parametrization Using a Single UV-Vis-NIR Transmittance Spectrum.

Molecules (Basel, Switzerland)·2026

Same author

High-Accuracy Characterization of a Single Thin Film on a Substrate from One Transmittance Spectrum by an Advanced Envelope Method Addressing Voids, Tail Electron Transitions, and Deep-Level Electron Transitions in a-Si Films.

Nanomaterials (Basel, Switzerland)·2026

Same author

Investigating Structurally and Pigmentary Colored Featherworks via Noninvasive Methodologies.

ACS omega·2026

Same author

Optimizing atrial fibrillation detection through ECG feature selection using Extra-Trees and statistical association measures.

Journal of electrocardiology·2026

Same author

Predicting substance use behaviors with machine learning using small sets of judgment and contextual variables.

Npj mental health research·2026

Same author

Automated HFrEF Diagnosis Using an Optimized TimeSformer Model in Echocardiography.

Journal of imaging informatics in medicine·2025

Search research articles

Related Experiment Video

Updated: Aug 15, 2025

Comprehensive Characterization of Extended Defects in Semiconductor Materials by a Scanning Electron Microscope

Comprehensive Characterization of Extended Defects in Semiconductor Materials by a Scanning Electron Microscope

Published on: May 28, 2016

Learning-based physical models of room-temperature semiconductor detectors with reduced data.

Srutarshi Banerjee¹, Miesher Rodrigues², Manuel Ballester³

¹Northwestern University, 2145 Sheridan Road, Evanston, IL, 60208, USA. srutarshibanerjee2022@u.northwestern.edu.

Scientific Reports

|January 4, 2023

Summary

This study introduces a novel learning-based physical model for room-temperature semiconductor radiation detectors (RTSD). It enables precise characterization of charge transport properties and material defects, even with limited data.

More Related Videos

Additive Manufacturing-Enabled Low-Cost Particle Detector

Additive Manufacturing-Enabled Low-Cost Particle Detector

Published on: March 24, 2023

High-resolution Thermal Micro-imaging Using Europium Chelate Luminescent Coatings

High-resolution Thermal Micro-imaging Using Europium Chelate Luminescent Coatings

Published on: April 16, 2017

Related Experiment Videos

Last Updated: Aug 15, 2025

Comprehensive Characterization of Extended Defects in Semiconductor Materials by a Scanning Electron Microscope

Comprehensive Characterization of Extended Defects in Semiconductor Materials by a Scanning Electron Microscope

Published on: May 28, 2016

Additive Manufacturing-Enabled Low-Cost Particle Detector

Additive Manufacturing-Enabled Low-Cost Particle Detector

Published on: March 24, 2023

High-resolution Thermal Micro-imaging Using Europium Chelate Luminescent Coatings

High-resolution Thermal Micro-imaging Using Europium Chelate Luminescent Coatings

Published on: April 16, 2017

Area of Science:

Materials Science
Semiconductor Physics
Detector Technology

Background:

Room-temperature semiconductor radiation detectors (RTSDs) are crucial for applications in medical imaging, security, and astrophysics.
Current characterization methods for RTSDs like CdZnTe and CdTe are labor-intensive, focusing on bulk properties rather than micron-level details.
Sub-pixel level 3-D event reconstruction requires detailed understanding of material defects and charge transport properties.

Purpose of the Study:

To develop a microscopic, learning-based physical model for RTSDs that overcomes limitations of current characterization techniques.
To enable precise material property characterization using limited experimental data.
To facilitate sub-pixel level analysis for improved 3-D event reconstruction in pixelated detectors.

Main Methods:

A novel learning-based physical model incorporating charge transport with trapping centers was developed.
Material properties were learned indirectly from measurable electrode signals and charge distributions during electron-hole pair injection.
The RTSD was spatially segmented into voxels, with material properties modeled as learnable parameters within each voxel.

Main Results:

The model successfully characterized charge drifts, trapping, detrapping, and recombination coefficients.
Characterization was achieved considering multiple trapping centers or a single equivalent trapping center.
The model's ability to characterize the RTSD ranged from complete to equivalent, depending on the amount of training data used.

Conclusions:

The proposed learning-based physical model offers an efficient and data-driven approach for characterizing RTSDs at a microscopic level.
This method significantly reduces the labor intensity associated with traditional characterization techniques.
The findings pave the way for enhanced 3-D event reconstruction and improved performance in various RTSD applications.