Computerized glow curve deconvolution (CGCD): A comparison using asymptotic vs rational approximation in thermoluminescence kinetic models

Wazir-ud-Din, M.; Ur-Rehman, Shakeel; Mahmood, M. Masood; Ahmad, Khalil; Hayat, Sikander; Siddique, Muhammad Tariq; Kakakhel, Muhammad Basim; Mirza, Sikander M.

doi:10.1016/j.apradiso.2021.110014

“…Deconvolution of the glow curve demands a rigorous analysis to resolve into its individual peaks and calculate the parameters of traps/localized states and several codes and algorithms have been developed to fit the glow curve and extract the parameters of the localized states. [65][66][67][68][69][70] Since the focus of this article is to review TL applications in semiconductors, demonstrate its advantages in this area, and guide the reader on data interpretation and their physical meaning, the mathematical description of TL and the different methods for trap parameter calculations will not be discussed here. For that, we refer the reader to Ref.…”

Section: Basics Of Tl and C-tsps And Measurement Techniquesmentioning

confidence: 99%

Advanced Thermoluminescence Spectroscopy as a Research Tool for Semiconductor and Photonic Materials: A Review and Perspective

Selim

¹

2023

Physica Status Solidi (a)

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Thermoluminescence (TL) or thermally stimulated luminescence (TSL) spectroscopy is based on liberating charge carriers from traps in the bandgap by providing enough thermal energy to overcome the potential barrier of the traps. It provides a powerful tool to measure the positions of the localized states/traps in the bandgap. Despite that, its applications in semiconductors are very limited. Herein, the basics of TL spectroscopy and the recent advances in the technique with focus on cryogenic thermally stimulated photoemission spectroscopy (C‐TSPS) which extends TL measurements to cryogenic regime and allows the detection of very low concentrations of shallow and deep localized states is discussed. One goal herein is to introduce the reader to the use of TL and C‐TSPS in the characterization of semiconductors, explaining how it can be applied and demonstrating its advantages as a powerful tool for measuring shallow donor/acceptor ionization energies in semiconductors and as a method for characterizing compensating defects. The article also discusses interesting potential applications of C‐TSPS in new research areas such as corrosion and formation of oxide layers on metal surfaces.

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“…Potential applications of curve fitting are countless and encompass virtually all scientific disciplines. Examples include biosynthesis, 6 thermoluminescence, 7 solar energy, 8 materials science and technology, 9 agriculture, 10 cancer research, 11 kinetics, 12 thermal engineering, 13 transportation, 14 soil science, 15 remote sensing of ecosystems, 16 epidemiology, 17 power and energy engineering, 18 population growth 19 and spectroscopy, 20 to name just a few. The disagreement metrics to minimize during the fit depends on the properties of the noise and possibly on prior information on the parameters to fit.…”

Section: Mainmentioning

confidence: 99%

Correcting Systematic Errors by Hybrid 2D Correlation Loss Functions in nonlinear inverse modelling

Mayerhöfer

¹

,

Noda

²

,

Pahlow

³

et al. 2022

Preprint

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Recently a new family of loss functions called smart error sums has been suggested. These loss functions account for correlations within experimental data and force modeled data to obey these correlations. As a result, multiplicative systematic errors of experimental data can be revealed and corrected. The smart error sums are based on 2D correlation analysis which is a comparably recent methodology for analyzing spectroscopic data that has found broad application. In this contribution we mathematically generalize and break down this methodology and the smart error sums to uncover the mathematic roots and simplify it to craft a general tool beyond spectroscopic modelling. This reduction also allows a simplified discussion about limits and prospects of this new method including one of its potential future uses as a sophisticated loss function in deep learning. To support its deployment, the work includes computer code to allow reproduction of the basic results.

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“…Potential applications of curve fitting are countless and encompass virtually all scientific disciplines. Examples include biosynthesis [ 9 ], thermoluminescence [ 10 ], solar energy [ 11 ], materials science and technology [ 12 ], agriculture [ 13 ], cancer research [ 14 ], kinetics [ 15 ], thermal engineering [ 16 ], transportation [ 17 ], soil science [ 18 ], remote sensing of ecosystems [ 19 ], epidemiology [ 20 ], power and energy engineering [ 21 ], population growth [ 22 ] and spectroscopy [ 23 ], to name just a few. The disagreement metrics to minimize during the fit depends on the properties of the noise and possibly on prior information on the parameters to fit.…”

Section: Introductionmentioning

confidence: 99%

Correcting systematic errors by hybrid 2D correlation loss functions in nonlinear inverse modelling

Mayerhöfer

¹

,

Noda

²

,

Pahlow

³

et al. 2023

View full text Add to dashboard Cite

Recently a new family of loss functions called smart error sums has been suggested. These loss functions account for correlations within experimental data and force modeled data to obey these correlations. As a result, multiplicative systematic errors of experimental data can be revealed and corrected. The smart error sums are based on 2D correlation analysis which is a comparably recent methodology for analyzing spectroscopic data that has found broad application. In this contribution we mathematically generalize and break down this methodology and the smart error sums to uncover the mathematic roots and simplify it to craft a general tool beyond spectroscopic modelling. This reduction also allows a simplified discussion about limits and prospects of this new method including one of its potential future uses as a sophisticated loss function in deep learning. To support its deployment, the work includes computer code to allow reproduction of the basic results.

show abstract

Computerized glow curve deconvolution (CGCD): A comparison using asymptotic vs rational approximation in thermoluminescence kinetic models

Cited by 12 publications

References 26 publications

Advanced Thermoluminescence Spectroscopy as a Research Tool for Semiconductor and Photonic Materials: A Review and Perspective

Advanced Thermoluminescence Spectroscopy as a Research Tool for Semiconductor and Photonic Materials: A Review and Perspective

Correcting Systematic Errors by Hybrid 2D Correlation Loss Functions in nonlinear inverse modelling

Correcting systematic errors by hybrid 2D correlation loss functions in nonlinear inverse modelling

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