Studien zu Memristoren und Fehlanpassungen : Schaltverhalten, Herstellung, optische Anwendung und Toleranz in FPUT-Systemen

Abstract

Inspired by biological neural networks, neuromorphic computing has emerged as a promising alternative to conventional architectures, which increasingly struggle to meet the needs of low power consumption, parallel processing, and real-time operation. The realisation of neuromorphic systems requires new types of devices, which have the capability of simultaneously storing and processing information. It is, however, not easily available from conventional Complementary Metal-Oxide-Semiconductor (CMOS)-based technologies. Memristor is a promising candidate for such systems, owing to its synapse-like, tunable resistance states and compatibility with CMOS technology. This cumulative dissertation investigates the fabrication, characterisation, and integration of memristor for neuromorphic and optical applications. For practical integration, the materials and fabrication techniques have been selected to be compatible with standard CMOS technology. The work first focuses on understanding the bidirectional switching behaviour of individual redox-based memristors and examines how the dielectric layer thickness influences device performance. Following these, a experimental realisation of an intelligent photodetector is achieved, which integrates a photodiode with a memristor to enable adaptive passive quenching. Meanwhile, a CMOS-compatible photodiode operating in Geiger mode is also designed for this purpose. Furthermore, this dissertation explores the Fermi–Pasta–Ulam–Tsingou (FPUT) system under mismatched condition, thereby addressing the theoretical foundations for building a nonlinear system with mismatch. Tolerance effects are analysed using Monte Carlo simulations, and a method is proposed to recover the energy recurrence behaviour despite such variability. This dissertation includes five peer-reviewed journal and conference papers that combine numerical and experimental research. Through the co-design of novel devices and non-linear systems, these works collectively lay the foundation for the future development of neuromorphic systems