Atomic Layer Deposition of Multicomponent Oxide Materials

Atomic layer deposition (ALD) is a method for thin film deposition which has been extensively studied for binary oxide thin film growth. Studies on multicomponent oxide growth by ALD remain relatively few owing to the increased number of factors that come into play when more than one metal is employed. More metal precursors are required, and the surface may change significantly during successive stages of the growth. Multicomponent oxide thin films can be prepared in a well-controlled way as long as the same principle that makes binary oxide ALD work so well is followed for each constituent element: in short, the film growth has to be self-limiting. ALD of various multicomponent oxides was studied. SrTiO3, BaTiO3, Ba(1-x)SrxTiO3 (BST), SrTa2O6, Bi4Ti3O12, BiTaO4 and SrBi2Ta2O9 (SBT) thin films were prepared, many of them for the first time by ALD. Chemistries of the binary oxides are shown to influence the processing of their multicomponent counterparts. The compatibility of precursor volatilities, thermal stabilities and reactivities is essential for multicomponent oxide ALD, but it should be noted that the main reactive species, the growing film itself, must also be compatible with self-limiting growth chemistry. In the cases of BaO and Bi2O3 the growth of the binary oxide was very difficult, but the presence of Ti or Ta in the growing film made self-limiting growth possible. The application of the deposited films as dielectric and ferroelectric materials was studied. Post-deposition annealing treatments in different atmospheres were used to achieve the desired crystalline phase or, more generally, to improve electrical properties. Electrode materials strongly influenced the leakage current densities in the prepared metal insulator metal (MIM) capacitors. Film permittivities above 100 and leakage current densities below 110-7 A/cm2 were achieved with several of the materials.

Optimisation Problems in Wireless Sensor Networks : Local Algorithms and Local Graphs

This thesis studies optimisation problems related to modern large-scale distributed systems, such as wireless sensor networks and wireless ad-hoc networks. The concrete tasks that we use as motivating examples are the following: (i) maximising the lifetime of a battery-powered wireless sensor network, (ii) maximising the capacity of a wireless communication network, and (iii) minimising the number of sensors in a surveillance application. A sensor node consumes energy both when it is transmitting or forwarding data, and when it is performing measurements. Hence task (i), lifetime maximisation, can be approached from two different perspectives. First, we can seek for optimal data flows that make the most out of the energy resources available in the network; such optimisation problems are examples of so-called max-min linear programs. Second, we can conserve energy by putting redundant sensors into sleep mode; we arrive at the sleep scheduling problem, in which the objective is to find an optimal schedule that determines when each sensor node is asleep and when it is awake. In a wireless network simultaneous radio transmissions may interfere with each other. Task (ii), capacity maximisation, therefore gives rise to another scheduling problem, the activity scheduling problem, in which the objective is to find a minimum-length conflict-free schedule that satisfies the data transmission requirements of all wireless communication links. Task (iii), minimising the number of sensors, is related to the classical graph problem of finding a minimum dominating set. However, if we are not only interested in detecting an intruder but also locating the intruder, it is not sufficient to solve the dominating set problem; formulations such as minimum-size identifying codes and locating dominating codes are more appropriate. This thesis presents approximation algorithms for each of these optimisation problems, i.e., for max-min linear programs, sleep scheduling, activity scheduling, identifying codes, and locating dominating codes. Two complementary approaches are taken. The main focus is on local algorithms, which are constant-time distributed algorithms. The contributions include local approximation algorithms for max-min linear programs, sleep scheduling, and activity scheduling. In the case of max-min linear programs, tight upper and lower bounds are proved for the best possible approximation ratio that can be achieved by any local algorithm. The second approach is the study of centralised polynomial-time algorithms in local graphs these are geometric graphs whose structure exhibits spatial locality. Among other contributions, it is shown that while identifying codes and locating dominating codes are hard to approximate in general graphs, they admit a polynomial-time approximation scheme in local graphs.

Exhaled nitric oxide : Variability and association with bronchial hyperresponsiveness and atopy

Airway inflammation is a key feature of bronchial asthma. In asthma management, according to international guidelines, the gold standard is anti-inflammatory treatment. Currently, only conventional procedures (i.e., symptoms, use of rescue medication, PEF-variability, and lung function tests) were used to both diagnose and evaluate the results of treatment with anti-inflammatory drugs. New methods for evaluation of degree of airway inflammation are required. Nitric oxide (NO) is a gas which is produced in the airways of healthy subjects and especially produced in asthmatic airways. Measurement of NO from the airways is possible, and NO can be measured from exhaled air. Fractional exhaled NO (FENO) is increased in asthma, and the highest concentrations are measured in asthmatic patients not treated with inhaled corticosteroids (ICS). Steroid-treated patients with asthma had levels of FENO similar to those of healthy controls. Atopic asthmatics had higher levels of FENO than did nonatopic asthmatics, indicating that level of atopy affected FENO level. Associations between FENO and bronchial hyperresponsiveness (BHR) occur in asthma. The present study demonstrated that measurement of FENO had good reproducibility, and the FENO variability was reasonable both short- and long-term in both healthy subjects and patients with respiratory symptoms or asthma. We demonstrated the upper normal limit for healthy subjects, which was 12 ppb calculated from two different healthy study populations. We showed that patients with respiratory symptoms who did not fulfil the diagnostic criteria of asthma had FENO values significantly higher than in healthy subjects, but significantly lower than in asthma patients. These findings suggest that BHR to histamine is a sensitive indicator of the effect of ICS and a valuable tool for adjustment of corticosteroid treatment in mild asthma. The findings further suggest that intermittent treatment periods of a few weeks’ duration are insufficient to provide long-term control of BHR in patients with mild persistent asthma. Moreover, during the treatment with ICS changes in BHR and changes in FENO were associated. FENO level was associated with BHR measured by a direct (histamine challenge) or indirect method (exercise challenge) in steroid-naïve symptomatic, non-smoking asthmatics. Although these associations could be found only in atopics, FENO level in nonatopic asthma was also increased. It can thus be concluded that assessment of airway inflammation by measuring FENO can be useful for clinical purposes. The methodology of FENO measurements is now validated. Especially in those patients with respiratory symptoms who did not fulfil the diagnostic criteria of asthma, FENO measurement can aid in treatment decisions. Serial measurement of FENO during treatment with ICS can be a complementary or an alternative method for evaluation in patients with asthma.

The Path to a Porous Semiconductor Multilayer

Thin films are the basis of much of recent technological advance, ranging from coatings with mechanical or optical benefits to platforms for nanoscale electronics. In the latter, semiconductors have been the norm ever since silicon became the main construction material for a multitude of electronical components. The array of characteristics of silicon-based systems can be widened by manipulating the structure of the thin films at the nanoscale - for instance, by making them porous. The different characteristics of different films can then to some extent be combined by simple superposition. Thin films can be manufactured using many different methods. One emerging field is cluster beam deposition, where aggregates of hundreds or thousands of atoms are deposited one by one to form a layer, the characteristics of which depend on the parameters of deposition. One critical parameter is deposition energy, which dictates how porous, if at all, the layer becomes. Other parameters, such as sputtering rate and aggregation conditions, have an effect on the size and consistency of the individual clusters. Understanding nanoscale processes, which cannot be observed experimentally, is fundamental to optimizing experimental techniques and inventing new possibilities for advances at this scale. Atomistic computer simulations offer a window to the world of nanometers and nanoseconds in a way unparalleled by the most accurate of microscopes. Transmission electron microscope image simulations can then bridge this gap by providing a tangible link between the simulated and the experimental. In this thesis, the entire process of cluster beam deposition is explored using molecular dynamics and image simulations. The process begins with the formation of the clusters, which is investigated for Si/Ge in an Ar atmosphere. The structure of the clusters is optimized to bring it as close to the experimental ideal as possible. Then, clusters are deposited, one by one, onto a substrate, until a sufficiently thick layer has been produced. Finally, the concept is expanded by further deposition with different parameters, resulting in multiple superimposed layers of different porosities. This work demonstrates how the aggregation of clusters is not entirely understood within the scope of the approximations used in the simulations; yet, it is also shown how the continued deposition of clusters with a varying deposition energy can lead to a novel kind of nanostructured thin film: a multielemental porous multilayer. According to theory, these new structures have characteristics that can be tailored for a variety of applications, with precision heretofore unseen in conventional multilayer manufacture.

Optimisation problems in wireless sensor networks

This thesis studies optimisation problems related to modern large-scale distributed systems, such as wireless sensor networks and wireless ad-hoc networks. The concrete tasks that we use as motivating examples are the following: (i) maximising the lifetime of a battery-powered wireless sensor network, (ii) maximising the capacity of a wireless communication network, and (iii) minimising the number of sensors in a surveillance application. A sensor node consumes energy both when it is transmitting or forwarding data, and when it is performing measurements. Hence task (i), lifetime maximisation, can be approached from two different perspectives. First, we can seek for optimal data flows that make the most out of the energy resources available in the network; such optimisation problems are examples of so-called max-min linear programs. Second, we can conserve energy by putting redundant sensors into sleep mode; we arrive at the sleep scheduling problem, in which the objective is to find an optimal schedule that determines when each sensor node is asleep and when it is awake. In a wireless network simultaneous radio transmissions may interfere with each other. Task (ii), capacity maximisation, therefore gives rise to another scheduling problem, the activity scheduling problem, in which the objective is to find a minimum-length conflict-free schedule that satisfies the data transmission requirements of all wireless communication links. Task (iii), minimising the number of sensors, is related to the classical graph problem of finding a minimum dominating set. However, if we are not only interested in detecting an intruder but also locating the intruder, it is not sufficient to solve the dominating set problem; formulations such as minimum-size identifying codes and locating–dominating codes are more appropriate. This thesis presents approximation algorithms for each of these optimisation problems, i.e., for max-min linear programs, sleep scheduling, activity scheduling, identifying codes, and locating–dominating codes. Two complementary approaches are taken. The main focus is on local algorithms, which are constant-time distributed algorithms. The contributions include local approximation algorithms for max-min linear programs, sleep scheduling, and activity scheduling. In the case of max-min linear programs, tight upper and lower bounds are proved for the best possible approximation ratio that can be achieved by any local algorithm. The second approach is the study of centralised polynomial-time algorithms in local graphs – these are geometric graphs whose structure exhibits spatial locality. Among other contributions, it is shown that while identifying codes and locating–dominating codes are hard to approximate in general graphs, they admit a polynomial-time approximation scheme in local graphs.