PhD defence: InGaAs Nanowire and Quantum Well Devices

Published: 2022-05-23

Thesis title: InGaAs Nanowire and Quantum Well Devices

Author: Lasse Södergren, Department of Electrical and Information Technology, Lund university

Faculty opponent: Associate Professor Sanghyeon Kim, South Korea.

Location: E:1406 E-huset, Ole Römers väg 3, LTH, Lund University, Lund.

Streamed at: https://lu-se.zoom.us/j/66974888842

Thesis for download (PDF)

Abstract

To fulfill the increasing demand for high-speed electronics used for computation or communication is one everlasting challenge for the semiconductor industry. Emerging fields such as quantum computation also has a need for circuits operating at cryogenic temperatures. The metal-oxide-semiconductor field-effect transistor (MOSFET) is the main component in modern electronics, traditionally fabricated in Si. However, III-V materials generally exhibits higher electron mobility compared to Si. This enables the realization of MOSFETs with higher operational speed or lower power consumption. While a nanowire geometry, where the channel is gated from multiple sides brings an increase in the electrostatic gate control, allowing for further gate length scaling. In this thesis, lateral InGaAs nanowire and quantum well devices have been fabricated and characterized with the purpose of understanding the electron transport and its limitations over a wide temperature range. MOSFETs at cryogenic temperatures, where the phonon occupation is low, are highly sensitive to disorder and defects in the semiconductor/oxide interface. InGaAs RF MOSFETs with different spacer technologies for reducing capacitances have also been fabricated and characterized. Optimizing the spacers for low capacitance and low access resistance is a key design consideration when fabricating devices for high-frequency operation.

PhD defence: Parametric Radio Channel Estimation and Robust Localization

Published: 2022-05-23

Thesis title: Parametric Radio Channel Estimation and Robust Localization

Author: Xuhong Li, Department of Electrical and Information Technology, Lund university

Faculty opponent: Professor Andrea Conti, Italy.

Location: E:1406 E-huset, Ole Römers väg 3, LTH, Lund University, Lund.

Streamed at: https://lu-se.zoom.us/j/64099598515 

Thesis for download (PDF)

Abstract

Robust and accurate localization using radio signals for scenarios such as indoor and dense urban areas is of great importance, but challenging due to multipath propagation and dynamic channel conditions. This thesis explores a few interesting topics in this research field both theoretically and experimentally, which are summarized in the following.

The first topic focuses on the estimation of local geometry related information conveyed in specular multipath components (MPCs) from channel observations for multipath-assisted localization and mapping. In dynamic scenarios, the number of existing specular MPCs (model-order) as well as their parameters, e.g., distances, angles and amplitudes, are both unknown and time-varying. The estimation quality of above unknown information largely influences the achievable accuracy and robustness of a localization solution.

The 5G-and-beyond radio systems exploit large-scale antenna arrays with up to a few hundred elements enabling superior resolvability of MPCs in angular subspace. We present an extended Kalman filter-based sequential parametric channel estimator exploiting phase information of MPCs and demonstrate the potential of using massive multiple-input multiple-output (MIMO) systems with standard cellular bandwidth for high-accuracy localization and mapping. Furthermore, it is noted that most of the existing parametric channel estimators essentially consider the model-order detection, data association, and sequential estimation of MPC parameters, but solve them in separate blocks. We proposes a belief propagation (BP)-based algorithm which formulates all the problems in a joint Bayesian framework, and obtains the marginal posterior probability density functions (PDFs) in an approximate but computationally efficient manner by running sum-product algorithm on the factor graph representation of the joint problem formulation. Moreover, the use of amplitude information further enables the reliable detection of ?weak? MPCs with very low signal-to-noise ratios. Results using real radio measurements demonstrate the excellent performance of the proposed algorithms in realistic and challenging scenarios.

The second topic concerns about received signal strength (RSS)-based localization solutions for long-range outdoor IoT networks. Such networks serve applications with low-power, low-rate and low-cost features, therefore dedicated localization solutions should have low complexity and minimum infrastructural needs. To make the best use of the limited resources, we present a localization solution which fuses both range and angle information extracted from non-coherent RSS measurements, and it is designed to be adaptive to dynamic propagation conditions by sequentially estimating the time-varying path-loss exponents for different anchors together with the target position.