Fondamentaux de la microscopie électronique à balayage

Table des matières

Chapter 1 Introduction
1.1 Background
1.2 Aim of this Work
1.3 Thesis Organization
1.4 Thesis Contribution
Chapter references
Chapter 2 Scanning Electron Microscopy fundamentals
2.1 Introduction
2.2 Interaction of the incident electrons with the irradiated matter
2.2.1 Physical process
2.2.2 function of energy dissipation
2.2.3 electron range
2.2.4 generation of pairs
2.2.5 Generation volume
2.3 Cathodoluminescence (CL)
2.3.1 Physical process
2.3.2 Formation of the CL signal
2.3.3 Calculation of the CL signal using Hergert et al model
2.4 Electron beam induced current (EBIC)
2.4.1 Physical phenomena
2.4.2 Calculation of the EBIC in a normal collector p-n junction configuration
2.4.2.1 The charge collection probability of Donolato
2.4.2.2 Calculation of the EBIC
Chapter references
Chapter 3 Artificial Neural Networks
3.1 Introduction
3.2 The neuron
3.3 Neural networks topology
3.4 Feedforward networks
3.4.1 Single layer feedforward networks
3.4.2 Multilayer feedforward networks
3.5 The learning process
3.6 The learning algorithm
3.6 The backpropagation algorithm
3.6 The Levenberg Marquardt algorithm
3.7 ANN for function approximation
3.7.1 System identification
3.7.2 Inverse modeling
Chapter references
Chapter 4 Genetic algorithms
4.1 Introduction
4.2 The GA operators
4.2.1 Selection
4.2.2 Crossover
4.2.3 Mutation
4.3 The GA parameters
4.3.1 Population options
4.3.2 Fitness scaling options
4.3.3 Selection options
4.3.4 Crossover options
4.3.5 Mutation options
4.3.6 Stopping criteria options
4.4 The continuous GA algorithm
Chapter references
Chapter 5 Semiconductor parameter extraction using artificial neural networks and exhaustive search
5.1 Introduction
5.2 Parameter extraction based on ANN and exhaustive search
5.2.1 Preparation of the training and test data sets
5.2.2 Training the ANN algorithm
5.2.3 Testing the ANN algorithm
5.2.4 Oversampling of the signal using ANN
5.2.5 Parameter extraction through exhaustive search
5.3 Application to cathodoluminescence
5.3.1 Training the ANN
5.3.2 Testing the algorithm
5.3.3 Oversampling of the CL signal
5.3.4 Exhaustive search
5.3.5 Effect of measurment noise
5.4 Application to EBIC
5.4.1 Training the ANN
5.4.2 Testing the algorithm
5.4.3 Oversampling of EBIC
5.4.4 Exhaustive search
5.5 Conclusion
Chapter references
Chapter 6 Semiconductor parameter extraction using artificial neural networks and inverse modeling
6.1 Introduction
6.2 Parameter extraction based on ANN and inverse modeling
6.2.1 Preparation of the training and test data sets
6.2.2 Training the ANN algorithm
6.2.3 Testing the ANN algorithm
6.3 Application to cathodoluminescence
6.3.1 Training the algorithm
6.3.2 Testing the algorithm
6.4 Application to EBIC
6.4.1 Training the algorithm
6.4.2 Testing the algorithm
6.5 Conclusion
Chapter references
Chapter 7 Semiconductor parameter extraction using genetic algorithms
7.1 Introduction
7.2 Parameter extraction using genetic algorithms
7.2.1 Initialize the parameters
7.2.2 Define the objective function
7.2.3 Apply the genetic algorithm
7.2.4 Extract the solution
7.3 Application to cathodoluminescence Effect of the initial population size
7.4 Application to EBIC
7.5 Conclusion
Chapter references
Chapter 8 Conclusion and future work
8.1 Conclusion
8.2 Future work
Author publications
Appendix A