The fast evolution of medical micro- and nanorobots in the endeavor to perform non-invasive medical operations in living organisms has boosted the use of diverse medical imaging techniques in the last years. Among those techniques, photoacoustic imaging (PAI), considered a functional technique, has shown to be promising for the visualization of micromotors in deep tissue with high spatiotemporal resolution as it possesses the molecular specificity of optical methods and the penetration depth of ultrasound. However, the precise maneuvering and function's control of medical micromotors, in particular in living organisms, require both anatomical and functional imaging feedback. Therefore, herein, the use of high-frequency ultrasound and PAI is reported to obtain anatomical and molecular information, respectively, of magnetically-driven micromotors in vitro and under ex vivo tissues. Furthermore, the steerability of the micromotors is demonstrated by the action of an external magnetic field into the uterus and bladder of living mice in real-time, being able to discriminate the micromotors’ signal from one of the endogenous chromophores by multispectral analysis. Finally, the successful loading and release of a model cargo by the micromotors toward non-invasive in vivo medical interventions is demonstrated.

Systemic chemotherapy remains the backbone of many cancer treatments. Due to its untargeted nature and the severe side effects it can cause, numerous nanomedicine approaches have been developed to overcome these issues. However, targeted delivery of therapeutics remains challenging. Engineering microrobots is increasingly receiving attention in this regard. Their functionalities, particularly their motility, allow microrobots to penetrate tissues and reach cancers more efficiently. Here, we highlight how different microrobots, ranging from tailor-made motile bacteria and tiny bubble-propelled microengines to hybrid spermbots, can be engineered to integrate sophisticated features optimised for precision-targeting of a wide range of cancers. Towards this, we highlight the importance of integrating clinicians, the public and cancer patients early on in the development of these novel technologies.

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Sprachtechnologie ist eine große und wachsende Industrie, die das Leben von technologieinteressierten Nutzern auf zahlreichen Wegen bereichert. Viele potenzielle Nutzer werden jedoch ausgeschlossen: Nämlich alle Sprecher, die nur schwer oder sogar gar nicht Sprache produzieren können. Silent-Speech Interfaces bieten einen Weg, mit Maschinen durch ein bequemes sprachgesteuertes Interface zu kommunizieren ohne dafür akustische Sprache zu benötigen. Sie können außerdem prinzipiell eine Ersatzstimme stellen, indem sie die intendierten Äußerungen, die der Nutzer nur still artikuliert, künstlich synthetisieren. Diese Dissertation stellt ein neues Silent-Speech Interface vor, das auf einem neu entwickelten Messsystem namens Elektro-Optischer Stomatografie und einem neuartigen parametrischen Vokaltraktmodell basiert, das die Echtzeitsynthese von Sprache basierend auf den gemessenen Daten ermöglicht. Mit der Hardware wurden Studien zur Einzelworterkennung durchgeführt, die den Stand der Technik in der intra- und inter-individuellen Genauigkeit erreichten und übertrafen. Darüber hinaus wurde eine Studie abgeschlossen, in der die Hardware zur Steuerung des Vokaltraktmodells in einer direkten Artikulation-zu-Sprache-Synthese verwendet wurde. Während die Verständlichkeit der Synthese von Vokalen sehr hoch eingeschätzt wurde, ist die Verständlichkeit von Konsonanten und kontinuierlicher Sprache sehr schlecht. Vielversprechende Möglichkeiten zur Verbesserung des Systems werden im Ausblick diskutiert.:Statement of authorship iii Abstract v List of Figures vii List of Tables xi Acronyms xiii 1. Introduction 1 1.1. The concept of a Silent-Speech Interface . . . . . . . . . . . . . . . . . . . . . . . . . . . 4 1.2. Structure of this work . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4 2. Fundamentals of phonetics 7 2.1. Components of the human speech production system . . . . . . . . . . . . . . . . . . . 7 2.2. Vowel sounds . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9 2.3. Consonantal sounds . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10 2.4. Acoustic properties of speech sounds . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15 2.5. Coarticulation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 18 2.6. Phonotactics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 19 2.7. Summary and implications for the design of a Silent-Speech Interface (SSI) . . . . . . . 21 3. Articulatory data acquisition techniques in Silent-Speech Interfaces 25 3.1. Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 25 3.2. Scope of the literature review . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 27 3.3. Video Recordings . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 27 3.4. Ultrasonography . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 30 3.5. Electromyography . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 34 3.6. Permanent-Magnetic Articulography . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 41 3.7. Electromagnetic Articulography . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 44 3.8. Radio waves . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 47 3.9. Palatography . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 49 3.10.Conclusion and Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 52 4. Electro-Optical Stomatography 55 4.1. Contact sensors . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 55 4.2. Optical distance sensors . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 57 4.3. Lip sensor . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 81 4.4. Sensor Unit . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 84 4.5. Control Unit . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 89 4.6. Software . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 93 5. Articulation-to-Text 99 5.1. Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 99 5.2. Command word recognition pilot study . . . . . . . . . . . . . . . . . . . . . . . . . . . . 99 5.3. Command word recognition small-scale study . . . . . . . . . . . . . . . . . . . . . . . . 102 6. Articulation-to-Speech 109 6.1. Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 109 6.2. Articulatory synthesis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 109 6.3. The six point vocal tract model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 113 6.4. Objective evaluation of the vocal tract model . . . . . . . . . . . . . . . . . . . . . . . . 116 6.5. Perceptual evaluation of the vocal tract model . . . . . . . . . . . . . . . . . . . . . . . . 120 6.6. Direct synthesis using EOS to control the vocal tract model . . . . . . . . . . . . . . . . 125 6.7. Pitch and voicing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 132 7. Summary and outlook 145 7.1. Summary of the contributions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 145 7.2. Outlook . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 146 A. Overview of the International Phonetic Alphabet 151 B. Mathematical proofs and derivations 153 B.1. Combinatoric calculations illustrating the reduction of possible syllables using phonotactics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 153 B.2. Signal Averaging . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 155 B.3. Effect of the contact sensor area on the conductance . . . . . . . . . . . . . . . . . . . . 155 B.4. Calculation of the forward current for the OP280V diode . . . . . . . . . . . . . . . . . . 155 C. Schematics and layouts 157 C.1. Schematics of the control unit. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 158 C.2. Layout of the control unit . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 163 C.3. Bill of materials of the control unit . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 164 C.4. Schematics of the sensor unit . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 165 C.5. Layout of the sensor unit . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 166 C.6. Bill of materials of the sensor unit . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 167 D. Sensor unit assembly 169 E. Firmware flow and data protocol 177 F. Palate file format 181 G. Supplemental material regarding the vocal tract model 183 H. Articulation-to-Speech: Optimal hyperparameters 189 Bibliography 191 / Speech technology is a major and growing industry that enriches the lives of technologically-minded people in a number of ways. Many potential users are, however, excluded: Namely, all speakers who cannot easily or even at all produce speech. Silent-Speech Interfaces offer a way to communicate with a machine by a convenient speech recognition interface without the need for acoustic speech. They also can potentially provide a full replacement voice by synthesizing the intended utterances that are only silently articulated by the user. To that end, the speech movements need to be captured and mapped to either text or acoustic speech. This dissertation proposes a new Silent-Speech Interface based on a newly developed measurement technology called Electro-Optical Stomatography and a novel parametric vocal tract model to facilitate real-time speech synthesis based on the measured data. The hardware was used to conduct command word recognition studies reaching state-of-the-art intra- and inter-individual performance. Furthermore, a study on using the hardware to control the vocal tract model in a direct articulation-to-speech synthesis loop was also completed. While the intelligibility of synthesized vowels was high, the intelligibility of consonants and connected speech was quite poor. Promising ways to improve the system are discussed in the outlook.:Statement of authorship iii Abstract v List of Figures vii List of Tables xi Acronyms xiii 1. Introduction 1 1.1. The concept of a Silent-Speech Interface . . . . . . . . . . . . . . . . . . . . . . . . . . . 4 1.2. Structure of this work . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4 2. Fundamentals of phonetics 7 2.1. Components of the human speech production system . . . . . . . . . . . . . . . . . . . 7 2.2. Vowel sounds . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9 2.3. Consonantal sounds . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10 2.4. Acoustic properties of speech sounds . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15 2.5. Coarticulation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 18 2.6. Phonotactics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 19 2.7. Summary and implications for the design of a Silent-Speech Interface (SSI) . . . . . . . 21 3. Articulatory data acquisition techniques in Silent-Speech Interfaces 25 3.1. Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 25 3.2. Scope of the literature review . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 27 3.3. Video Recordings . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 27 3.4. Ultrasonography . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 30 3.5. Electromyography . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 34 3.6. Permanent-Magnetic Articulography . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 41 3.7. Electromagnetic Articulography . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 44 3.8. Radio waves . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 47 3.9. Palatography . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 49 3.10.Conclusion and Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 52 4. Electro-Optical Stomatography 55 4.1. Contact sensors . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 55 4.2. Optical distance sensors . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 57 4.3. Lip sensor . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 81 4.4. Sensor Unit . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 84 4.5. Control Unit . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 89 4.6. Software . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 93 5. Articulation-to-Text 99 5.1. Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 99 5.2. Command word recognition pilot study . . . . . . . . . . . . . . . . . . . . . . . . . . . . 99 5.3. Command word recognition small-scale study . . . . . . . . . . . . . . . . . . . . . . . . 102 6. Articulation-to-Speech 109 6.1. Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 109 6.2. Articulatory synthesis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 109 6.3. The six point vocal tract model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 113 6.4. Objective evaluation of the vocal tract model . . . . . . . . . . . . . . . . . . . . . . . . 116 6.5. Perceptual evaluation of the vocal tract model . . . . . . . . . . . . . . . . . . . . . . . . 120 6.6. Direct synthesis using EOS to control the vocal tract model . . . . . . . . . . . . . . . . 125 6.7. Pitch and voicing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 132 7. Summary and outlook 145 7.1. Summary of the contributions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 145 7.2. Outlook . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 146 A. Overview of the International Phonetic Alphabet 151 B. Mathematical proofs and derivations 153 B.1. Combinatoric calculations illustrating the reduction of possible syllables using phonotactics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 153 B.2. Signal Averaging . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 155 B.3. Effect of the contact sensor area on the conductance . . . . . . . . . . . . . . . . . . . . 155 B.4. Calculation of the forward current for the OP280V diode . . . . . . . . . . . . . . . . . . 155 C. Schematics and layouts 157 C.1. Schematics of the control unit. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 158 C.2. Layout of the control unit . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 163 C.3. Bill of materials of the control unit . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 164 C.4. Schematics of the sensor unit . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 165 C.5. Layout of the sensor unit . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 166 C.6. Bill of materials of the sensor unit . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 167 D. Sensor unit assembly 169 E. Firmware flow and data protocol 177 F. Palate file format 181 G. Supplemental material regarding the vocal tract model 183 H. Articulation-to-Speech: Optimal hyperparameters 189 Bibliography 191

The realization of wireless ultra-reliable low-latency communications (URLLC) is one of the key challenges of the fifth generation (5G) of mobile communications systems and beyond. Ensuring ultra-high reliability together with a latency in the (sub-)millisecond range is expected to enable self-driving cars, wireless factory automation, and the Tactile Internet. In wireless communications, reliability is usually only considered as percentage of successful packet delivery, aiming for 1 − 10⁻⁵ up to 1 − 10⁻⁹ in URLLC.

With the advancement of semiconductor technology, the System on Chip (SoC) is becoming more and more complex, so the on-chip communication has become a bottleneck of SoC Design. Since the traditional bus system is inefficient and not scalable, the Network-On-Chip (NoC) has emerged as the promising communication mechanism for complex SoCs. As some systems have specific performance requirements, such as a minimum throughput (for real-time streaming data) or bounded latency (for interrupts, process synchronization, etc), communication with Guaranteed Service (GS) support becomes crucial for predictable SoC architectures. Circuit Switching (CS) is a popular approach to support GS, which firstly has to allocate an exclusively connection (circuit) between the source and destination nodes, and then the data packets are delivered over this connection. However, it is inefficient and inflexible because the resource is occupied by single connection during its whole lifetime, which can block other communications. Hence, two extensions of CS have been proposed to share resources: i) Time-Division Multiplexing (TDM), in which the available link capacity is split into multiple time slots to be shared by different flows in TDM scheme; and ii) Space-Division-Multiplexing (SDM), in which only a subset (sub-channel) of the link wires is exclusively allocated to a specific connection, while the remaining wires of the link can be used by other flows. The connection allocation is critical for CS, since the data delivery can start only after the associated connection is allocated. In this thesis, we propose a dedicated hardware connection allocator to solve the dynamic connection allocation problem for CS NoCs, which has to i) allocate a contention-free path between source-destination pairs and ii) allocate appropriate portions of link bandwidth (appropriate number of time slots and subsets) along the path. The dedicated connection allocator, called NoCManager, solves the connection allocation problem by employing a trellis-search based shortest path algorithm. The trellis search can explore all possible paths between source node and destination. Moreover, it shall find the requested path in a fixed low latency and can guarantee the path optimality in terms of path length if the path is available. In this thesis, two different trellis graphs, Forward-Backtrack trellis and Register-Exchange trellis are proposed. The Forward-Backtrack trellis completes the path search in two steps: forward search and backtracking. Firstly, the forward search begins at source node that traverses the network to find the free path. When destination node is reached, the backtrack starts from destination to select the survivor path and collect the associated path parameters. However, Register-Exchange trellis saves the entire survivor path sequences during forward search. Consequently, the backtracking step can be omitted, and thus the allocation time is halved compared to forward-backtrack approaches. Moreover, each trellis graph consists of three categories, unfolded structure, folded structure and bidirectional structure. The unfolded structure can provide high allocation speed while folded structure is more efficient from a hardware point of view. The bidirectional structure starts the search at two sides, source node and destination node simultaneously, so the allocation speed is 2 times faster than previous unidirectional search. Furthermore, in order to address the scalability issue of previous centralized systems, the partitioned architecture (i.e. spatial partitioning technique) is proposed to divide the large system into multiple smaller differentiated logical partitions served by local NoCManagers. This partitioning technique keeps the request load of the manager and manager-node communication overhead moderate. Inside each partition, the path search problem is solved by a local manager with trellis-search algorithm. To establish a path that crosses partitions, the managers communicate with each other in distributed manner to converge the global path. In order to further enhance the path diversity and resource utilization, we adopt the combined TDM and SDM technique. In combined TDM-SDM approach, each SDM sub-channel is split into multiple time slots so that can be shared by multiple flows. Hence, the number of sub-channels can be kept moderate to reduce router complexity, while still providing higher path diversity than TDM scheme. In order to investigate and optimize TDM-SDM partitioning strategy, we studied the influence of different TDM-SDM link partitioning strategies on success rate and path length that allowed us to find the optimal solution. The dedicated connection allocator using the trellis-search algorithm is employed for TDM, SDM and TDM-SDM CS. In the end, we present the router architecture that combines the circuit-switching network (for GS communication) and packet-switching network (for best-effort communication).

The monolithic integration of III-V semiconductors on Si substrates is a part of a long-term technological roadmap for the semiconductor industry towards More-than-Moore technologies. Despite of the different lattice constants and thermal expansion coefficients, research efforts over the last two decades have shown that III-V crystals with a high structural quality can be grown epitaxially in the form of nanowires directly on Si using CMOS-compatible (Au-free) methods. Among other III-V compounds, InxGa1-xAs is of the special interest for the use in infrared photonics and high-speed electronics due to its tunable direct bandgap and low electron effective mass, respectively. For comparison, InxGa1-xAs thin films are typically grown on lattice-matched InP substrates with a limited range of compositions at around x=0.52. The realization of InxGa1-xAs nanowires on Si, though, has been proved challenging owing to the limited In-content when the nanowires are grown Ga-catalyzed or the high density of stacking faults when the nanowires are grown catalyst-free. In this work, the use of highly lattice-mismatched GaAs/InxGa1-xAs and GaAs/InxAl1-xAs core/shell nanowires on Si(111) substrates have been studied as an alternative to InxGa1-xAs nanowires. The core/shell mismatch strain and its accommodation within the nanowires plays an important role in the growth, the structural, and the electronic properties of the nanowires. A key parameter in this work was the unusually small diameter of 20 – 25 nm of the GaAs core. First, the strain-induced bending of the nanowires during the growth of the shell by molecular beam epitaxy was investigated. It was apparent that the nanowires bend as a result of a preferential incorporation of In adatoms on one side of the nanowires. To obtain straight nanowires with symmetric shell composition and thickness around the core, it was necessary to choose relatively low growth temperatures and high growth rates that limited the surface diffusivity of In adatoms. Second, the strain accommodation in straight nanowires was investigated as a function of the shell thickness and composition using a combination of Raman scattering spectroscopy and X-ray diffraction. For a fixed shell composition of x=0.20 and small enough shell thicknesses, the strain in the shell is compressive and decreases progressively as the shell grows thicker. On the other hand, the strain in the core is tensile with hydrostatic character and increases with shell thickness. Finally, for shell thicknesses larger than 40 nm, the shell becomes strain-free, whereas the strain in the core saturates at 3.2% without any dislocations. For a fixed shell thickness of 80 nm, the strain in the core was further increased by increasing the In-content in the shell, reaching values as high as 7% for x=0.54. A plastic relaxation via misfit dislocations was observed only for the next highest In-content of x=0.70. In agreement to theoretical predictions, the tensile strain in the core resulted in a large reduction of the GaAs bandgap (as measured by photoluminescence spectroscopy), up to approximately 40% of the strain-free value. A similar reduction in electron effective mass is also expected. The transport properties of electrons inside the strained GaAs core were assessed by optical-pump terahertz-probe spectroscopy. Quite high mobility values of approximately 6100 cm2/Vs at 300 K for a carrier concentration of 9×1017 cm−3 were measured, which are the highest reported in the literature for GaAs nanowires, but also higher than the values for unstrained bulk GaAs. The importance of the results in this work is two-fold. On the one hand, strain-free InxGa1-xAs nanowire shells were grown on Si substrates with x up to 0.54 and thicknesses well beyond the critical thickness of their thin film counterparts. Such shells could potentially be employed as conduction channels in high electron mobility transistors (HEMTs) integrated in Si platforms. On the other hand, highly tensile-strained GaAs cores with electronic properties like those of InxGa1-xAs thin films were obtained. In this case, the results demonstrate, that GaAs nanowires can be suitable for photonic devices across the near-infrared range, including telecom photonics at 1.3 and potentially 1.55 μm, as well as for high-speed electronics. GaAs as a binary material is expected to be advantageous compared to InxGa1-xAs due to the absence of structural imperfections typically present in ternary alloys. Finally, to explore the potential of the core/shell nanowires as HEMTs, self-consistent Schrödinger-Poisson calculations of two different modulation-doped heterostructures were performed. In the case of a strained GaAs core overgrown by an unstrained InxGa1-xAs shell and an additional unstrained Si-doped InxAl1-xAs shell, the possibility to form a cylindrical-like two-dimensional electron gas inside the InxGa1-xAs shell was found. In the alternative case of a strained GaAs core overgrown by an unstrained Si-doped InxAl1-xAs shell, it was found that it is possible to form a quasi-one-dimensional electron gas at the center of the core. Both structures are the subject of ongoing research.:1 Introduction 1 2 Fundamentals and state-of-the-art 7 2.1 Electronic and structural properties of III-V semiconductors 7 2.2 Growth of III-V nanowires on Si 20 2.3 Core/shell heterostructure nanowires 29 2.4 Strain in epilayers and core/shell nanowires 36 2.5 Strain engineering in core/shell nanowires and its effect on band parameters 46 2.6 Modulation-doped III-V semiconductor heterostructures 56 3 Methods 61 3.1 Optical and electron microscopes 61 3.2 X-ray diffraction 64 3.3 Raman scattering spectroscopy 65 3.4 Photoluminescence spectroscopy 75 3.5 Optical-pump terahertz-probe spectroscopy and photoconductivity in semiconductors 77 3.6 Device processing 82 3.7 Semiconductor nanodevice software “nextnano” 85 3.8 MBE for crystal growth and core/shell nanowire growth 86 4 Results and discussions 91 4.1 Structural, compositional analyses of straight nanowires and coherent growth limit 91 4.2 Bent nanowires 95 4.3 Strain analyses in core/shell nanowires 97 4.3.1 Dependence of strain on shell thickness 97 4.3.2 Dependence of strain on the shell chemical composition 102 4.3.3 Dependence of strain on the core diameter 105 4.4 Strain-induced modification of electronic properties 106 4.5 Strain-enhanced electron mobility of GaAs nanowires higher than the bulk limit 114 4.6 Towards high electron mobility transistors 123 5 Conclusion and outlook 129 Bibliography 131 List of abbreviations I List of Symbols III List of publications VII List of conference contributions VIII Acknowledgements X

We report on a two-layer based ferroelectric tunnel junction with hafnium zirconium oxide (HZO) as the ferroelectric layer and aluminum oxide as the tunneling layer. The experimental results focus on optimizing the thicknesses of the layer stack. The device operation relies on the polarization reversal of the HZO layer, while electron tunneling occurs through the dielectric layer. The ferroelectric response of the HZO shows high remanent polarization values and good endurance with only weak wake-up and fatigue behavior. Adding the additional dielectric tunneling layer, the device becomes operational as a ferroelectric tunnel junction in the nanoampere current range. It shows good on/off ratios and promising retention behavior, paving the way for future applications as a polarization-based resistive memory device.

The feasibility of the QD-Flash concept, its fast write and erase times, is demonstrated together with storage times of 4 days at room temperature. The storage time of holes in (InGa)Sb QDs embedded in a (AlGa)P matrix can be extended by growth modifications to 10 y. Tunneling structures were recently demonstrated to solve the trade-off conflict between storage time and erase time. A QD-NVSRAM is suggested to become the first commercial application.

The number of subscribers and use cases of mobile communication networks are expanding expeditiously with the evolution of technology. The available spectrum in lower frequency ranges does not meet the unprecedented increase in demand for user data throughput in mobile networks. Facing the problem of limited spectrum in traditional cellular bands that are below 6 GHz, Millimeter Wave (mmWave) frequency bands are being standardized for the 5th Generation (5G) mobile networks as a promising means for handling the unprecedented data traffic surge. Enabling higher carrier frequencies introduces new channel conditions. Propagating signals are exposed to higher diffraction loss and are highly susceptible to blockage caused by surrounding objects, which leads to rapid signal degradation and challenges user mobility. On the other hand, higher carrier frequencies enable the deployment of many small-sized antennas that are used for directional signal transmission, resulting in beamforming gain. In recent studies, a conditional handover procedure has been adopted for 5G networks to enhance user mobility robustness. Besides, contention-free random access procedure has been defined for beamformed systems aiming at minimizing the signaling and service interruption time caused by the random access procedure. An improper configuration of the mobility parameters, e.g., handover preparation and execution offsets, access beam selection threshold of random access procedure, leads User Equipments (UEs) to experience Handover Failures (HOFs) and Radio Link Failures (RLFs), and causes unnecessary signaling and inefficient resource utilization in the network. Each cell border has unique propagation characteristics and user mobility pattern, and, therefore, mobility parameters should be configured for each cell border individually. Moreover, mobility parameters should be updated for dynamic propagation environment (e.g., construction of buildings, seasonal changes in the vegetation) and for temporal mobility patterns. Considering the individual cell border configuration, temporal adaptation of the mobility parameters, and ultra-dense deployment, optimization of the conditional handover and random access parameters is a complex task that cannot be carried by human interaction. Therefore, an automatic optimization of the parameters is needed where the network collects statistics of the mobility events and adjusts the parameters autonomously. To investigate user mobility under these new propagation conditions, a proper model is needed that captures spatial and temporal characteristics of the channel in beamformed networks. Current channel models that have been developed for 5G networks are too detailed for the purpose of mobility simulations and lead to infeasible simulation time for most user mobility simulations. In this work, a simplified channel model is presented that captures the spatial and temporal characteristics of the 5G propagation channel and runs in feasible simulation time. To this end, the coherence time and path diversity originating from a fully fledged Geometry-based Stochastic Channel Model (GSCM) are analyzed and adopted in Jake’s channel model with reduced computational complexity. Furthermore, the deviation of multipath beamforming gain from single ray beamforming gain is analyzed and a regression curve is obtained to be used in the system-level simulations. In a typical system-level mobility simulator, the average downlink signal-to-interference and noise ratio (SINR) is used for RLF detection and throughput calculation. In addition to the channel model, models of desired and interfering signals are formulated first, by considering the impact of antenna beamforming, and a closed-form expression of average downlink SINR is derived by taking into account the user and beam scheduling probabilities. Then, an accurate approximation of the average downlink SINR with low computational complexity is presented, for 5G networks where the base station forms multiple beams. In addition, an SINR model is derived for both strict and opportunistic resource-fair scheduler, where the latter targets a higher utilization of radio resources when multiple beams are scheduled simultaneously. The mobility performance of conditional handover and contention-free random access are investigated by using the proposed channel and SINR models. Besides, a resource efficient random access procedure is proposed that aims at maximizing the utilization of contention-free random access resources. Moreover, simple, yet, effective decision tree-based supervised learning method is proposed to minimize the HOFs that are caused by the beam preparation phase of the random access procedure. Similarly, a decision-tree-based supervised learning method is proposed for automatic optimization of the conditional handover parameters. In addition, enhanced logging and emergency reporting methods are introduced first time in this study to mitigate the cell detection problems that are caused by rapid signal degradation. Results show that the optimum operation point of random access (in terms of minimizing the HOFs and maximizing the random access resource utilization) is achievable with the proposed learning algorithm for random access procedure in conditional handover. Results also show that the mobility performance of conditional handover is improved by automatic optimization of the handover parameters. In addition, the proposed enhanced logging and emergency reporting methods mitigate the mobility problems related with cell detection and further improve the mobility performance in combination with the decision-tree-based supervised learning methods.