Impurity Density Profiling
Impurity Density Profiling - Related Videos
09:14
Glovebox and Impurity Sensors
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2023
Source: Tamara M. Powers, Department of Chemistry, Texas A&M University The glovebox provides a straightforward means to handle air- and moisture-sensitive solids and liquids. The glovebox is what it sounds like: a box with gloves attached to one or more sides, which allows the user to perform manipulations within the glovebox under an inert atmosphere. For manipulations under inert atmospheres, chemists can choose between Schlenk or high-vacuum techniques and a glovebox. Schlenk and...
07:10
3D Depth Profile Reconstruction of Segregated Impurities Using Secondary Ion Mass Spectrometry
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2020
The presented method describes how to identify and solve measurement artifacts related to secondary ion mass spectrometry as well as obtain realistic 3D distributions of impurities/dopants in solid state materials.
11:42
Fabrication of Gate-tunable Graphene Devices for Scanning Tunneling Microscopy Studies with Coulomb Impurities
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Cited by 13 •
2015
This paper details the fabrication process of a gate-tunable graphene device, decorated with Coulomb impurities for scanning tunneling microscopy studies. Mapping the spatially dependent electronic structure of graphene in the presence of charged impurities unveils the unique behavior of its relativistic charge carriers in response to a local Coulomb potential.
08:42
Density Gradient Ultracentrifugation
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2023
Density gradient ultracentrifugation is a common technique used to isolate and purify biomolecules and cell structures. This technique exploits the fact that, in suspension, particles that are more dense than the solvent will sediment, while those that are less dense will float. A high-speed ultracentrifuge is used to accelerate this process in order to separate biomolecules within a density gradient, which can be established by layering liquids of decreasing density in a centrifuge tube. The...
03:03
Generating an Ultra-Low-Density Neuronal Culture Using a High-Density Neuronal Feeder Layer
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2025
This video demonstrates the method for culturing ultra-low-density neurons in the presence of a high-density neuronal feeder layer. It establishes a co-culture of varying-density neurons, ensuring close physical proximity. The growth factors secreted by high-density neurons help neuronal survival and growth, maintaining ultra-low-density neurons for a longer time period.
FAQs
The connection depends on pairing a controlled sectioning or probing strategy with measurements taken at known locations through the material. Electrical signals from capacitance–voltage or spreading-resistance analysis, and chemical signals from secondary-ion mass spectrometry, can then be related to local impurity concentration. This depth-resolved relationship exposes changes that a single bulk measurement would conceal.
These approaches provide complementary ways to relate local measurements to impurity distribution. Capacitance–voltage and spreading-resistance methods use electrical behavior, whereas secondary-ion mass spectrometry supplies a chemical measurement. Comparing profiles provides alternative electrical and chemical evidence, strengthening interpretation of gradients, junction locations, or processing changes identified within a material.
Engineers examine doping gradients, junction locations, activation behavior, and signatures associated with diffusion or implantation effects. These features connect the measured depth distribution with fabrication conditions and device behavior. A profile can therefore show how impurities vary across regions relevant to a junction, supporting assessment of how processing influenced the material and its electronic performance.
A typical workflow selects a region for controlled sectioning or probing, measures the resulting electrical or chemical response, and relates each measurement to material depth. The chosen approach may use capacitance–voltage analysis, spreading-resistance measurements, or secondary-ion mass spectrometry. The resulting depth profile is then examined for gradients, junctions, activation, diffusion, or implantation effects.
It is useful when engineers need evidence about fabrication or device behavior that depends on impurity placement through depth. Applications include semiconductor process control, device failure analysis, and materials characterization. Teams can also use the profiles to optimize fabrication conditions, especially when assessing diffusion, implantation effects, doping gradients, or junction locations related to reliable electronic performance.
Because the profile preserves variation with depth, it supports evaluation of gradients, junction locations, and activation behavior rather than only an overall material condition. Engineers can compare these features with diffusion or implantation effects and use the findings in process control or failure analysis. This spatial perspective helps link material characterization to fabrication optimization and electronic reliability.