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Deep-Level Transient Spectroscopy

Authors: Johan Lauwaert; Samira Khelifi;

Deep-Level Transient Spectroscopy

Abstract

This chapter discusses deep-level transient spectroscopy (DLTS). After describing the DLTS theory, it also discusses different methods to analyze the recorded transients and how background illumination can help to assign levels to the same defect structure. The chapter covers the determination of the capture cross sections and the observation of minority carrier traps. It provides information on different signals that deviate from a single carrier trap. One of which is the high-resolution DLTS or Laplace DLTS methodology. It is obvious that the resolution is the major advantage of Laplace DLTS. In conventional DLTS single levels, especially those with the highest activation energy, can result in broader peaks. DLTS observes the emission of carriers from deep levels. Therefore, it is often not straightforward to assign them to a certain defect structure or impurity. The method for fitting the evolution of the DLTS-signal as a function of filling pulse can take into account small variations in the deep level concentration.

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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
0
Average
Average
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