- AutorIn
- Melanie Herzig
- Titel
- Optimization of niobium oxide-based threshold switches for oscillator-based applications
- Zitierfähige Url:
- https://nbn-resolving.org/urn:nbn:de:bsz:14-qucosa2-885603
- Erstveröffentlichung
- 2023
- Datum der Einreichung
- 16.11.2021
- Datum der Verteidigung
- 08.04.2022
- Abstract (EN)
- In niobium oxide-based capacitors non-linear switching characteristics can be observed if the oxide properties are adjusted accordingly. Such non-linear threshold switching characteristics can be utilized in various non-linear circuit applications, which have the potential to pave the way for the application of new computing paradigms. Furthermore, the non-linearity also makes them an interesting candidate for the application as selector devices e.g. for non-volatile memory devices. To satisfy the requirements for those two areas of application, the threshold switching characteristics need to be adjusted to either obtain a maximized voltage extension of the negative differential resistance region in the quasi-static I-V characteristics, which enhances the non-linearity of the devices and results in improved robustness to device-to-device variability or to adapt the threshold voltage to a specific non-volatile memory cell. Those adaptations of the threshold switching characteristics were successfully achieved by deliberate modifications of the niobium oxide stack. Furthermore, the impact of the material stack on the dynamic behavior of the threshold switches in non-linear circuits as well as the impact of the electroforming routine on the threshold switching characteristics were analyzed. The optimized device stack was transferred from the micrometer-sized test structures to submicrometer-sized devices, which were packaged to enable easy integration in complex circuits. Based on those packaged threshold switching devices the behavior of single as well as of coupled relaxation oscillators was analyzed. Subsequently, the obtained results in combination with the measurement results for the statistic device-to-device variability were used as a basis to simulate the pattern formation in coupled relaxation oscillator networks as well as their performance in solving graph coloring problems. Furthermore, strategies to adapt the threshold voltage to the switching characteristics of a tantalum oxide-based non-volatile resistive switch and a non-volatile phase change cell, to enable their application as selector devices for the respective cells, were discussed.
- Verweis
- Improvement of NbOx -based threshold switching devices by implementing multilayer stacks
Link: https://iopscience.iop.org/article/10.1088/1361-6641/ab1da3
DOI: 10.1088/1361-6641/ab1da3 - Multiple slopes in the negative differential resistance region of NbOx-based threshold switches
Link: https://iopscience.iop.org/article/10.1088/1361-6463/ab217a/meta
DOI: 10.1088/1361-6463/ab217a - Improved Vertex Coloring With NbOₓ Memristor-Based Oscillatory Networks
Link: https://ieeexplore.ieee.org/abstract/document/9371291
DOI: 10.1109/TCSI.2021.3061973 - Pattern Formation With Locally Active S-Type NbOₓ Memristors
DOI: 10.1109/TCSI.2019.2894218
Link: https://ieeexplore.ieee.org/abstract/document/8642334 - Analysis of Vth variability in NbOx -based threshold switches
DOI: 10.1109/NVMTS.2016.7781515
Link: https://iopscience.iop.org/article/10.1088/1361-6641/ab1da3 - Freie Schlagwörter (EN)
- Niobium oxide, threshold switching, neuromorphic computing, Frenkel-Poole conduction, relaxation oscillator
- Klassifikation (DDC)
- 621.3
- Klassifikation (RVK)
- ZN 5840
- GutachterIn
- Prof. Dr. Thomas Mikolajick
- Prof. Dr. Martin Ziegler
- Prof. Dr. Uwe Marschner
- Den akademischen Grad verleihende / prüfende Institution
- Technische Universität Dresden, Dresden
- Förder- / Projektangaben
- Deutsche Forschungsgemeinschaft Locally Active Memristive Data Processing
(LAMP)
ID: 273537230 - Version / Begutachtungsstatus
- publizierte Version / Verlagsversion
- URN Qucosa
- urn:nbn:de:bsz:14-qucosa2-885603
- Veröffentlichungsdatum Qucosa
- 11.12.2023
- Dokumenttyp
- Dissertation
- Sprache des Dokumentes
- Englisch
- Lizenz / Rechtehinweis
- CC BY-NC-ND 4.0
- Inhaltsverzeichnis
Abstract I Zusammenfassung II List of Abbrevations VI List of Symbols VII 1 Motivation 1 2 Basics 5 2.1 Negative differential resistance and local activity in memristor devices 5 2.2 Threshold switches as selector devices 8 2.3 Switching effects observed in NbOx 13 2.3.1 Threshold switching caused by metal-insulator transition 13 2.3.2 Threshold switching caused by Frenkel-Poole conduction 18 2.3.3 Non-volatile resistive switching 32 3 Sample preparation 35 3.1 Deposition techniques 35 3.1.1 Evaporation 35 3.1.2 Sputtering 36 3.2 Micrometer-sized devices 36 3.3 Submicrometer-sized devices 37 3.3.1 Process flow 37 3.3.2 Reduction of the electrode resistance 39 3.3.3 Transfer from structuring via electron beam lithography to structuring via laser lithography 48 3.3.4 Packaging procedure 50 4 Investigation and optimization of the electrical device characteristic 51 4.1 Introduction 51 4.2 Measurement setup 52 4.3 Electroforming 53 4.3.1 Optimization of the electroforming process 53 4.3.2 Characterization of the formed filament 62 4.4 Dynamic device characteristics 67 4.4.1 Emergence and measurement of dynamic behavior 67 4.4.2 Impact of the dynamic device characteristics on quasi-static I-V characteristics 70 5 Optimization of the material stack 81 5.1 Introduction 81 5.2 Adjustment of the oxygen content in the bottom layer 82 5.3 Influence of the thickness of the oxygen-rich niobium oxide layer 92 5.4 Multilayer stacks 96 5.5 Device-to-device and Sample-to-sample variability 110 6 Applications of NbOx-based threshold switching devices 117 6.1 Introduction 117 6.2 Non-linear circuits 117 6.2.1 Coupled relaxation oscillators 117 6.2.2 Memristor Cellular Neural Network 121 6.2.3 Graph Coloring 127 6.3 Selector devices 132 7 Summary and Outlook 138 8 References 141 9 List of publications 154 10 Appendix 155 10.1 Parameter used for the LT Spice simulation of I-V curves for threshold switches with varying oxide thicknesses 155 10.2 Dependence of the oscillation frequency of the relaxation oscillator circuit on the capacitance and the applied source voltage 156 10.3 Calculation of the oscillation frequency of the relaxation oscillator circuit 157 10.4 Characteristics of the memristors and the cells utilized in the simulation of the memristor cellular neural network 164 10.5 Calculation of the impedance of the cell in the memristor cellular network 166 10.6 Example graphs from the 2nd DIMACS series 179 11 List of Figures 182 12 List of Tables 194