Decentralized Narrowband and Wideband Spectrum Sensing with Correlated Observations

This dissertation evaluates the utility of several approaches to the design of good distributed sensing systems for both narrowband and wideband spectrum sensing problems with correlated sensor observations

Electrical Characterisation of III-V Nanowire MOSFETs

The ever increasing demand for faster and more energy-efficient electricalcomputation and communication presents severe challenges for the semiconductor industry and particularly for the metal-oxidesemiconductorfield-effect transistor (MOSFET), which is the workhorse of modern electronics. III-V materials exhibit higher carrier mobilities than the most commonly used MOSFET material Si so that the realisation of III-V MOSFETs can enable higher operation speeds and lower drive voltages than that which is possible in Si electronics. A lowering of the transistor drive voltage can be further facilitated by employing gate-all-around nanowire geometries or novel operation principles. However, III-V materials bring about their own challenges related to material quality and to the quality of the gate oxide on top of a III-V MOSFET channel.This thesis presents detailed electrical characterisations of two types of (vertical) III-V nanowire transistors: MOSFETs based on conventional thermionic emission; and Tunnel FETs, which utilise quantum-mechanical tunnelling instead to control the device current and reach inverse subthreshold slopes below the thermal limit of 60 mV/decade. Transistor characterisations span over fourteen orders of magnitude in frequency/time constants and temperatures from 11 K to 370 K.The first part of the thesis focusses on the characterisation of electrically active material defects (‘traps’) related to the gate stack. Low-frequency noise measurements yielded border trap densities of 10^18 to 10^20 cm^-3 eV^-1 and hysteresis measurements yielded effective trap densities – projected to theoxide/semiconductor interface – of 2x10^12 to 3x10^13 cm^-2 eV^-1. Random telegraph noise measurements revealed that individual oxide traps can locally shift the channel energy bands by a few millielectronvolts and that such defects can be located at energies from inside the semiconductor band gap all the way into the conduction band.Small-signal radio frequency (RF) measurements revealed that parts of the wide oxide trap distribution can still interact with carriers in the MOSFET channel at gigahertz frequencies. This causes frequency hystereses in the small-signal transconductance and capacitances and can decrease the RF gains by a few decibels. A comprehensive small-signal model was developed, which takes into account these dispersions, and the model was applied to guide improvements of the physical structure of vertical RF MOSFETs. This resulted in values for the cutoff frequency fT and the maximum oscillation frequency fmax of about 150 GHz in vertical III-V nanowire MOSFETs.Bias temperature instability measurements and the integration of (lateral) III-V nanowire MOSFETs in a back end of line process were carried out as complements to the main focus of this thesis. The results of this thesis provide a broad perspective of the properties of gate oxide traps and of the RF performance of III-V nanowire transistors and can act as guidelines for further improvement and finally the integration of III-V nanowire MOSFETs in circuits

Cutting Edge Nanotechnology

The main purpose of this book is to describe important issues in various types of devices ranging from conventional transistors (opening chapters of the book) to molecular electronic devices whose fabrication and operation is discussed in the last few chapters of the book. As such, this book can serve as a guide for identifications of important areas of research in micro, nano and molecular electronics. We deeply acknowledge valuable contributions that each of the authors made in writing these excellent chapters

Sensing the real world:inverse problems, sparsity and sensor placement

A sensor is a device that detects or measures a physical property and records, indicates, or otherwise responds to it. In other words, a sensor allows us to interact with the surrounding environment, by measuring qualitatively or quantitatively a given phenomena. Biological evolution provided every living entity with a set of sensors to ease the survival to daily challenges. In addition to the biological sensors, humans developed and designed “artificial” sensors with the aim of improving our capacity of sensing the real world. Today, thanks to technological developments, sensors are ubiquitous and thus, we measure an exponentially growing amount of data. Here is the challenge—how do we process and use this data? Nowadays, it is common to design real-world sensing architectures that use the measured data to estimate certain parameters of the measured physical field. This type of problems are known in mathematics as inverse problems and finding their solution is challenging. In fact, we estimate a set of parameters of a physical field with possibly infinite degrees of freedom with only a few measurements, that are most likely corrupted by noise. Therefore, we would like to design algorithms to solve the given inverse problem, while ensuring the existence of the solution, its uniqueness and its robustness to the measurement noise. In this thesis, we tackle different inverse problems, all inspired by real-world applications. First, we propose a new regularization technique for linear inverse problems based on the sensor placement optimization of the sensor network collecting the data. We propose Frame- Sense, a greedy algorithm inspired by frame theory that finds a near-optimal sensor placement with respect to the reconstruction error of the inverse problem solution in polynomial time. We substantiate our theoretical findings with numerical simulations showing that our method improves the state of the art. In particular, we show significant improvements on two realworld applications: the thermal monitoring of many-core processors and the adaptive sampling scheduling of environmental sensor networks. Second, we introduce the dual of the sensor placement problem, namely the source placement problem. In this case, instead of regularizing the inverse problem, we enable a precise control of the physical field by means of a forward problem. For this problem, we propose a near-optimal algorithm for the noiseless case, that is when we know exactly the current state of the physical field. Third, we consider a family of physical phenomena that can be modeled by means of graphs, where the nodes represent a set of entities and the edges model the transmission delay of an information between the entities. Examples of this phenomena are the spreading of a virus within the population of a given region or the spreading of a rumor on a social network. In this scenario, we identify two new key problems: the source placement and vaccination. For the former, we would like to find a set of sources such that the spreading of the information over the network is as fast as possible. For the latter, we look for an optimal set of nodes to be “vaccinated” such that the spreading of the virus is the slowest. For both problems, we propose greedy algorithms directly optimizing the average time of infection of the network. Such algorithms out-perform the current state of the art and we evaluate their performance with a set of experiments on synthetic datasets. Then, we discuss three distinct inverse problems for physical fields characterized by a diffusive phenomena, such as temperature of solid bodies or the dispersion of pollution in the atmosphere. We first study the uniform sampling and reconstruction of diffusion fields and we show that we can exploit the kernel of the field to control and bound the aliasing error. Second, we study the source estimation of a diffusive field given a set of spatio-temporal measurements of the field and under the assumption that the sources can be modeled as a set of Dirac’s deltas. For this estimation problem, we propose an algorithm that exploits the eigenfunctions representation of the diffusion field and we show that this algorithm recovers the sources precisely. Third, we propose an algorithm for the estimation of time-varying emissions of smokestacks from the data collected in the surrounding environment by a sensor network, under the assumption that the emission rates can be modeled as signals lying on low-dimensional subspaces or with a finite rate of innovation. Last, we analyze a classic non-linear inverse problem, namely the sparse phase retrieval. In such a problem, we would like to estimate a signal from just the magnitude of its Fourier transform. Phase retrieval is of interest for many scientific applications, such as X-ray crystallography and astronomy. We assume that the signal of interest is spatially sparse, as it happens for many applications, and we model it as a linear combination of Dirac’s delta. We derive sufficient conditions for the uniqueness of the solution based on the support of the autocorrelation function of the measured sparse signal. Finally, we propose a reconstruction algorithm for the sparse phase retrieval taking advantage of the sparsity of the signal of interest

Energy and Area Efficient Machine Learning Architectures using Spin-Based Neurons

Recently, spintronic devices with low energy barrier nanomagnets such as spin orbit torque-Magnetic Tunnel Junctions (SOT-MTJs) and embedded magnetoresistive random access memory (MRAM) devices are being leveraged as a natural building block to provide probabilistic sigmoidal activation functions for RBMs. In this dissertation research, we use the Probabilistic Inference Network Simulator (PIN-Sim) to realize a circuit-level implementation of deep belief networks (DBNs) using memristive crossbars as weighted connections and embedded MRAM-based neurons as activation functions. Herein, a probabilistic interpolation recoder (PIR) circuit is developed for DBNs with probabilistic spin logic (p-bit)-based neurons to interpolate the probabilistic output of the neurons in the last hidden layer which are representing different output classes. Moreover, the impact of reducing the Magnetic Tunnel Junction\u27s (MTJ\u27s) energy barrier is assessed and optimized for the resulting stochasticity present in the learning system. In p-bit based DBNs, different defects such as variation of the nanomagnet thickness can undermine functionality by decreasing the fluctuation speed of the p-bit realized using a nanomagnet. A method is developed and refined to control the fluctuation frequency of the output of a p-bit device by employing a feedback mechanism. The feedback can alleviate this process variation sensitivity of p-bit based DBNs. This compact and low complexity method which is presented by introducing the self-compensating circuit can alleviate the influences of process variation in fabrication and practical implementation. Furthermore, this research presents an innovative image recognition technique for MNIST dataset on the basis of p-bit-based DBNs and TSK rule-based fuzzy systems. The proposed DBN-fuzzy system is introduced to benefit from low energy and area consumption of p-bit-based DBNs and high accuracy of TSK rule-based fuzzy systems. This system initially recognizes the top results through the p-bit-based DBN and then, the fuzzy system is employed to attain the top-1 recognition results from the obtained top outputs. Simulation results exhibit that a DBN-Fuzzy neural network not only has lower energy and area consumption than bigger DBN topologies while also achieving higher accuracy