High-speed Channel Analysis and Design using Polynomial Chaos Theory and Machine Learning

With the exponential increase in the data rate of high-speed serial channels, their efficient and accurate analysis and design has become of crucial importance. Signal integrity analysis of these channels is often done with the eye diagram analysis, which demonstrates jitter and noise of the channel. Conventional methods for this type of analysis are either exorbitantly time and memory consuming, or only applicable to linear time invariant (LTI) systems. On the other hand, recently advancements in numerical methods and machine learning has shown a great potential for analysis and design of high-speed electronics. Therefore, in this dissertation we introduce two novel approaches for efficient eye analysis, based on machine learning and numerical techniques. These methods are focused on the data dependent jitter and noise, and the intersymbol interference. In the first approach, a complete surrogate model of the channel is trained using a short transient simulation. This model is based on the Polynomial Chaos theory. It can directly and quickly provide distribution of the jitter and other statistics of the eye diagram. In addition, it provides an estimation of the full eye diagram. The second analysis method is for faster analysis when we are interested in finding the worst-case eye width, eye height, and inner eye opening, which would be achieved by the conventional eye analysis if its transient simulation is continued for an arbitrary amount of time. The proposed approach quickly finds the data patterns resulting in the worst signal integrity; hence, in the closest eye. This method is based on the Bayesian optimization. Although majority of the contributions of this dissertation are on the analysis part, for the sake of completeness the final portion of this work is dedicated to design of high-speed channels with machine learning since the interference and complex interactions in modern channels has made their design challenging and time consuming too. The proposed design approach focuses on inverse design of CTLE, where the desired eye height and eye width are given, and the algorithm finds the corresponding peaking and DC gain of CTLE. This approach is based on the invertible neural networks. Main advantage of this network is the possibility to provide multiple solutions for cases where the answer to the inverse problem is not unique. Numerical examples are provided to evaluate efficiency and accuracy of the proposed approaches. The results show up to 11.5X speedup for direct estimation of the jitter distribution using the PC surrogate model approach. In addition, up to 23X speedup using the worst-case eye analysis approach is achieved, and the inverse design of CTLE shows promising results.Ph.D

Advanced RF and Microwave Design Optimization: A Journey and a Vision of Future Trends

In this paper, we outline the historical evolution of RF and microwave design optimization and envisage imminent and future challenges that will be addressed by the next generation of optimization developments. Our journey starts in the 1960s, with the emergence of formal numerical optimization algorithms for circuit design. In our fast historical analysis, we emphasize the last two decades of documented microwave design optimization problems and solutions. From that retrospective, we identify a number of prominent scientific and engineering challenges: 1) the reliable and computationally efficient optimization of highly accurate system-level complex models subject to statistical uncertainty and varying operating or environmental conditions; 2) the computationally-efficient EM-driven multi-objective design optimization in high-dimensional design spaces including categorical, conditional, or combinatorial variables; and 3) the manufacturability assessment, statistical design, and yield optimization of high-frequency structures based on high-fidelity multi-physical representations. To address these major challenges, we venture into the development of sophisticated optimization approaches, exploiting confined and dimensionally reduced surrogate vehicles, automated feature-engineering-based optimization, and formal cognition-driven space mapping approaches, assisted by Bayesian and machine learning techniques.ITESO, A.C

System level power integrity transient analysis using a physics-based approach

With decreasing supply voltage level and massive demanding current on system chipset, power integrity design becomes more and more critical for system stability. The ultimate goal of well-designed power delivery network (PDN) is to deliver desired voltage level from the source to destination, in other words, to minimize voltage noise delivered to digital devices. The thesis is composed of three parts. The first part focuses on-die level power models including simplified chip power model (CPM) for system level analysis and the worst scenario current profile. The second part of this work introduces the physics-based equivalent circuit model to simplify the passive PDN model to RLC circuit netlist, to be compatible with any spice simulators and tremendously boost simulation speed. Then a novel system/chip level end-to-end transient model is proposed, including the die model and passive PDN model discussed in previous two chapters as well as a SIMPLIS based small signal VRM model. In the last part of the thesis, how to model voltage regulator module (VRM) is explicitly discussed. Different linear approximated VRM modeling approaches have been compared with the SIMPLIS small signal VRM model in both frequency domain and time domain. The comparison provides PI engineers a guideline to choose specific VRM model under specific circumstances. Finally yet importantly, a PDN optimization example was given. Other than previous PDN optimization approaches, a novel hybrid target impedance concept was proposed in this thesis, in order to improve system level PDN optimization process --Abstract, page iv

Unmanned Aerial Vehicle (UAV)-Enabled Wireless Communications and Networking

The emerging massive density of human-held and machine-type nodes implies larger traffic deviatiolns in the future than we are facing today. In the future, the network will be characterized by a high degree of flexibility, allowing it to adapt smoothly, autonomously, and efficiently to the quickly changing traffic demands both in time and space. This flexibility cannot be achieved when the network’s infrastructure remains static. To this end, the topic of UAVs (unmanned aerial vehicles) have enabled wireless communications, and networking has received increased attention. As mentioned above, the network must serve a massive density of nodes that can be either human-held (user devices) or machine-type nodes (sensors). If we wish to properly serve these nodes and optimize their data, a proper wireless connection is fundamental. This can be achieved by using UAV-enabled communication and networks. This Special Issue addresses the many existing issues that still exist to allow UAV-enabled wireless communications and networking to be properly rolled out

Lidar-based Obstacle Detection and Recognition for Autonomous Agricultural Vehicles

Today, agricultural vehicles are available that can drive autonomously and follow exact route plans more precisely than human operators. Combined with advancements in precision agriculture, autonomous agricultural robots can reduce manual labor, improve workflow, and optimize yield. However, as of today, human operators are still required for monitoring the environment and acting upon potential obstacles in front of the vehicle. To eliminate this need, safety must be ensured by accurate and reliable obstacle detection and avoidance systems.In this thesis, lidar-based obstacle detection and recognition in agricultural environments has been investigated. A rotating multi-beam lidar generating 3D point clouds was used for point-wise classification of agricultural scenes, while multi-modal fusion with cameras and radar was used to increase performance and robustness. Two research perception platforms were presented and used for data acquisition. The proposed methods were all evaluated on recorded datasets that represented a wide range of realistic agricultural environments and included both static and dynamic obstacles.For 3D point cloud classification, two methods were proposed for handling density variations during feature extraction. One method outperformed a frequently used generic 3D feature descriptor, whereas the other method showed promising preliminary results using deep learning on 2D range images. For multi-modal fusion, four methods were proposed for combining lidar with color camera, thermal camera, and radar. Gradual improvements in classification accuracy were seen, as spatial, temporal, and multi-modal relationships were introduced in the models. Finally, occupancy grid mapping was used to fuse and map detections globally, and runtime obstacle detection was applied on mapped detections along the vehicle path, thus simulating an actual traversal.The proposed methods serve as a first step towards full autonomy for agricultural vehicles. The study has thus shown that recent advancements in autonomous driving can be transferred to the agricultural domain, when accurate distinctions are made between obstacles and processable vegetation. Future research in the domain has further been facilitated with the release of the multi-modal obstacle dataset, FieldSAFE

A two phase framework for visible light-based positioning in an indoor environment: performance, latency, and illumination

Recently with the advancement of solid state lighting and the application thereof to Visible Light Communications (VLC), the concept of Visible Light Positioning (VLP) has been targeted as a very attractive indoor positioning system (IPS) due to its ubiquity, directionality, spatial reuse, and relatively high modulation bandwidth. IPSs, in general, have 4 major components (1) a modulation, (2) a multiple access scheme, (3) a channel measurement, and (4) a positioning algorithm. A number of VLP approaches have been proposed in the literature and primarily focus on a fixed combination of these elements and moreover evaluate the quality of the contribution often by accuracy or precision alone. In this dissertation, we provide a novel two-phase indoor positioning algorithmic framework that is able to increase robustness when subject to insufficient anchor luminaries and also incorporate any combination of the four major IPS components. The first phase provides robust and timely albeit less accurate positioning proximity estimates without requiring more than a single luminary anchor using time division access to On Off Keying (OOK) modulated signals while the second phase provides a more accurate, conventional, positioning estimate approach using a novel geometric constrained triangulation algorithm based on angle of arrival (AoA) measurements. However, this approach is still an application of a specific combination of IPS components. To achieve a broader impact, the framework is employed on a collection of IPS component combinations ranging from (1) pulsed modulations to multicarrier modulations, (2) time, frequency, and code division multiple access, (3) received signal strength (RSS), time of flight (ToF), and AoA, as well as (4) trilateration and triangulation positioning algorithms. Results illustrate full room positioning coverage ranging with median accuracies ranging from 3.09 cm to 12.07 cm at 50% duty cycle illumination levels. The framework further allows for duty cycle variation to include dimming modulations and results range from 3.62 cm to 13.15 cm at 20% duty cycle while 2.06 cm to 8.44 cm at a 78% duty cycle. Testbed results reinforce this frameworks applicability. Lastly, a novel latency constrained optimization algorithm can be overlaid on the two phase framework to decide when to simply use the coarse estimate or when to expend more computational resources on a potentially more accurate fine estimate. The creation of the two phase framework enables robust, illumination, latency sensitive positioning with the ability to be applied within a vast array of system deployment constraints

Perceptual modelling for 2D and 3D

Livrable D1.1 du projet ANR PERSEECe rapport a été réalisé dans le cadre du projet ANR PERSEE (n° ANR-09-BLAN-0170). Exactement il correspond au livrable D1.1 du projet