Chalcogenide and metal-oxide memristive devices for advanced neuromorphic computing

Energy-intensive artificial intelligence (AI) is prevailing and changing the world, which requires energy-efficient computing technology. However, traditional AI driven by von Neumann computing systems suffers from the penalties of high-energy consumption and time delay due to frequent data shuttling. To tackle the issue, brain-inspired neuromorphic computing that performs data processing in memory is developed, reducing energy consumption and processing time. Particularly, some advanced neuromorphic systems perceive environmental variations and internalize sensory signals for localized in-senor computing. This methodology can further improve data processing efficiency and develop multifunctional AI products. Memristive devices are one of the promising candidates for neuromorphic systems due to their non-volatility, small size, fast speed, low-energy consumption, etc. In this thesis, memristive devices based on chalcogenide and metal-oxide materials are fabricated for neuromorphic computing systems. Firstly, a versatile memristive device (Ag/CuInSe2/Mo) is demonstrated based on filamentary switching. Non-volatile and volatile features are coexistent, which play multiple roles of non-volatile memory, selectors, artificial neurons, and artificial synapses. The conductive filaments’ lifetime was controlled to present both volatile and non-volatile behaviours. Secondly, the sensing functions (temperature and humidity) are explored based on Ag conductive filaments. An intelligent matter (Ag/Cu(In, Ga)Se2/Mo) endowing reconfigurable temperature and humidity sensations is developed for sensory neuromorphic systems. The device reversibly switches between two states with differentiable semiconductive and metallic features, demonstrating different responses to temperature and humidity variations. Integrated devices can be employed for intelligent electronic skin and in-sensor computing. Thirdly, the memristive-based sensing function of light was investigated. An optoelectronic synapse (ITO/ZnO/MoO3/Mo) enabling multi-spectrum sensitivity for machine vision systems is developed. For the first time, this optoelectronic synapse is practical for front-end retinomorphic image sensing, convolution processing, and back-end neuromorphic computing. This thesis will benefit the development of advanced neuromorphic systems pushing forward AI technology

Beyond "More than Moore": Novel applications of BiFeO3 (BFO)-based nonvolatile resistive switches

The size reduction of transistors has been the main reason for a successful development of semiconductor integrated circuits over the last decades. Because of the physically limited downscaling of transistors, alternative technologies namely the information processing and nonvolatile resistive switches (also termed memristors) have come into focus. Memristors reveal a fast switching speed, long retention time, and stable endurance. Nonvolatile analog bipolar resistive switching with a considerable large On/Off ratio is reported in BiFeO3 (BFO)-based resistive switches. So far resistive switches are mainly applied in memory applications or logic operations. Given the excellent properties of BFO based memristors, the further exploration of functionalities for memristive devices is required. A new approach for hardware based cryptographic system was developed within the framework of this dissertation. By studying the power conversion efficiencies on BFO memristor at various harmonics, it has been shown that two sets of clearly distinguishable power ratios are achievable when the BFO memristor is set into high or into low resistance state. Thus, a BFO-based binary encoding system can be established. As an example the unrecoverable seizure information from encoded medical data suggests the proper functioning of the proposed encryption system. Aside from cryptographic functionality, the single pairing spike timing dependent plasticity (STDP) in BFO-based artificial synapses is demonstrated, which can be considered as the cornerstone for energy-efficient and fast hardware-based neuromorphic networks. In comparison to the biological driven realistic way, only single one pairing of pre- and postsynaptic spikes is applied to the BFO-based artificial synapse instead of 60-80 pairings. Thus, the learning time constant of STDP function can be reduced from 25 ms to 125 us.In den letzten Jahrzehnten war die Größenreduktion von Transistoren einer der Hauptgründe für die Leistungssteigerung von integrierten Halbleiterschaltungen. Aufgrund des physikalisch beschränkten Skalierungspotentials, werden alternative Technologien für Halbleiterschaltungen entwickelt. Dazu zählen neuartige Widerstandsschalter, sogenannte Memristoren, welche wegen ihrer schnellen Schaltgeschwindigkeit, langen Speicherzeit und stabilen Haltbarkeit in den Fokus der Forschung gerückt sind. Das nichtflüchtige analoge bipolare Schalten des Widerstandwertes mit einem On/Off Verhältnis größer als 100 wurde in BiFeO 3 (BFO)-basierten Widerstands-schaltern beobachtet. Bisher wurden Widerstandsschalter hauptsächlich als Speicher oder in rekonfigurierbaren Logikschaltungen verwendet. Aufgrund der ausgezeichneten Eigenschaften von BFO-basierten Memristoren, ist die Untersuchung weiterer neuer Funktionalitäten vielversprechend. Als neuer Ansatz für ein Hardware-basiertes Kryptosystem wird in der vorliegenden Arbeit die Ausnutzung des Leistungsübertragungskoeffizienten in BFO Memristoren vorgeschlagen. Mit Hilfe der unterschiedlichen Oberschwingungen, welche von einem BFO Memristor im ON und OFF Zustand generiert werden, wurde ein Kryptosystem zum Kodieren binärer Daten entwickelt. Ein Test des Hardware-basierten Kryptosystems an Biodaten ergab, dass die kodierten Biodaten keine vorhersagbare Korrelation mehr enthielten. In der vorliegenden Arbeit wurden darüberhinaus BFO-basierte künstliche Synapsen mit einer Aktionspotentials-Intervall abhängigen Plastizität (STDP) für Einzelpulse entwickelt. Diese Einzelpuls-STDP legt den Grundstein für energieffiziente und schnelle neuromorphe Netzwerke mit künstlichen Synapsen. Im Vergleich zu biologischen Synapsen mit einer 60-80-Puls-STDP und einem Lernfenster auf der ms-Zeitskale, konnte das Lernfenster von BFO-basierten künstlichen Synapsen von 25 ms auf 125 μs reduziert werden. Solch ein schnelles Lernen ermöglicht auch die extreme Reduzierung des Leistungsverbrauchs in neuromorphen Netzwerken