A 5 GHz continuous unlicensed bandwidth is available at millimeter-wave (mm-wave) frequencies around 60 GHz and offers the prospect for multi gigabit wireless applications. The inherent atmospheric attenuation at 60 GHz due to oxygen absorption makes the frequency range ideal for short distance communication networks. For these mm-wave wireless networks, the low noise amplifier (LNA) is a critical subsystem determining the receiver performance i.e., the noise figure (NF) and receiver sensitivity. It however proves challenging to realise high performance mm-wave LNAs in a silicon (Si) complementary metal-oxide semiconductor (CMOS) technology. The mm-wave passive devices, specifically on-chip inductors, experience high propagation loss due to the conductivity of the Si substrate at mm-wave frequencies, degrading the performance of the LNA and subsequently the performance of the receiver architecture. The research is aimed at realising a high performance mm-wave LNA in a Si BiCMOS technology. The focal points are firstly, the fundamental understanding of the various forms of losses passive inductors experience and the techniques to address these issues, and secondly, whether the performance of mm-wave passive inductors can be improved by means of geometry optimising. An associated hypothesis is formulated, where the research outcome results in a preferred passive inductor and formulates an optimised passive inductor for mm-wave applications. The performance of the mm-wave inductor is evaluated using the quality factor (Q-factor) as a figure of merit. An increased inductor Q-factor translates to improved LNA input and output matching performance and contributes to the lowering of the LNA NF. The passive inductors are designed and simulated in a 2.5D electromagnetic (EM) simulator. The electrical characteristics of the passive structures are exported to a SPICE netlist which is included in a circuit simulator to evaluate and investigate the LNA performance. Two LNAs are designed and prototyped using the 13μ-m SiGe BiCMOS process from IBM as part of the experimental process to validate the hypothesis. One LNA implements the preferred inductor structures as a benchmark, while the second LNA, identical to the first, replaces one inductor with the optimised inductor. Experimental verification allows complete characterization of the passive inductors and the performance of the LNAs to prove the hypothesis. According to the author's knowledge, the slow-wave coplanar waveguide (S-CPW) achieves a higher Q-factor than microstrip and coplanar waveguide (CPW) transmission lines at mm-wave frequencies implemented for the 130 nm SiGe BiCMOS technology node. In literature, specific S-CPW transmission line geometry parameters have previously been investigated, but this work optimises the signal-to-ground spacing of the S-CPW transmission lines without changing the characteristic impedance of the lines. Optimising the S-CPW transmission line for 60 GHz increases the Q-factor from 38 to 50 in simulation, a 32 % improvement, and from 8 to 10 in measurements. Furthermore, replacing only one inductor in the output matching network of the LNA with the higher Q-factor inductor, improves the input and output matching performance of the LNA, resulting in a 5 dB input and output reflection coefficient improvement. Although a 5 dB improvement in matching performance is obtained, the resultant noise and gain performance show no significant improvement. The single stage LNAs achieve a simulated gain and NF of 13 dB and 5.3 dB respectively, and dissipate 6 mW from the 1.5 V supply. The LNA focused to attain high gain and a low NF, trading off linearity and as a result obtained poor 1 dB compression of -21.7 dBm. The LNA results are not state of the art but are comparable to SiGe BiCMOS LNAs presented in literature, achieving similar gain, NF and power dissipation figures. / Dissertation (MEng)--University of Pretoria, 2012. / Electrical, Electronic and Computer Engineering / unrestricted

Neuronen sind bei der Informationsübertragung des zentralen Nervensystems von entscheidender Bedeutung. Ihre Aktivität liegt der Signalverarbeitung und höheren kognitiven Prozessen zugrunde. Neuronen sind in den extrazellulären Raum eingebettet, der mehrere Teilchen, darunter auch Ionen, enthält. Ionenkonzentrationen sind nicht statisch. Intensive neuronale Aktivität kann intrazelluläre und extrazelluläre Ionenkonzentrationen verändern. In dieser Arbeit untersuche ich das Wechselspiel zwischen neuronaler Aktivität und der Dynamik der Ionenkonzentrationen. Dabei konzentriere ich mich hauptsächlich auf extrazelluläre Kalium- und intrazelluläre Natriumkonzentrationen. Mit Hilfe der Theorie dynamischer Systeme zeige ich, wie moderate Änderungen dieser Ionenkonzentrationen die neuronale Aktivität qualitativ verändern können, wodurch sich möglicherweise die Signalverarbeitung verändert. Dann modelliere ich ein leitfähigkeitsbasiertes neuronales Netzwerk mit Spikes. Das Modell sagt voraus, dass eine moderate Änderung der Konzentrationen, die einen Mikroschaltkreis von Neuronen umgeben, die Leistungsspektraldichte der Populationsaktivität verändern könnte. Insgesamt unterstreicht diese Arbeit die Bedeutung der Dynamik der Ionenkonzentrationen für das Verständnis neuronaler Aktivität auf langen Zeitskalen und liefert technische Erkenntnisse darüber, wie das Zusammenspiel zwischen ihnen modelliert und analysiert werden kann. / Neurons are essential in the information transfer mechanisms of the central nervous system. Their activity underlies both basic signal processing, and higher cognitive processes. Neurons are embedded in the extracellular space, which contains multiple particles, including ions which are vital to their functioning. Ionic concentrations are not static, intense neuronal activity alters the intracellular and extracellular ionic concentrations which in turn affect neuronal functioning. In this thesis, I study the interplay between neuronal activity and ionic concentration dynamics. I focus specifically on the extracellular potassium and intracellular sodium concentrations. Using dynamical systems theory, I illustrate how moderate changes in these ionic concentrations can qualitatively change neuronal activity, potentially altering signal processing. I then model a conductance-based spiking neural network. The model predicts that a moderate change in the concentrations surrounding a microcircuit of neurons could modify the power spectral density of the population activity. Altogether, this work highlights the need to consider ionic concentration dynamics to understand neuronal activity on long time scales and provides technical insights on how to model and analyze the interplay between them.