Algorithmic studies on PCB routing

As IC technology advances, the package size keeps shrinking while the pin count of a package keeps increasing. A modern IC package can have a pin count of thousands. As a result, a complex printed circuit board (PCB) can host more than ten thousand signal nets. Such a huge pin count and net count make manual design of packages and PCBs an extremely time-consuming and error-prone task. On the other hand, increasing clock frequency imposes various physical constraints on PCB routing. These constraints make traditional IC and PCB routers not applicable to modern PCB routing. To the best of our knowledge, there is no mature commercial or academic automated router that handles these constraints well. Therefore, automated PCB routers that are tuned to handle such constraints become a necessity in modern design. In this dissertation, we propose novel algorithms for three major aspects of PCB routing: escape routing, area routing and layer assignment. Escape routing for packages and PCBs has been studied extensively in the past. Network flow is pervasively used to model this problem. However, previous studies are incomplete in two senses. First, none of the previous works correctly model the diagonal capacity, which is essential for 45 degree routing in most packages and PCBs. As a result, existing algorithms may either produce routing solutions that violate the diagonal capacity or fail to output a legal routing even though one exists. Second, few works discuss the escape routing problem of differential pairs. In high-performance PCBs, many critical nets use differential pairs to transmit signals. How to escape differential pairs from a pin array is an important issue that has received too little attention in the literature. In this dissertation, we propose a new network flow model that guarantees the correctness when diagonal capacity is taken into consideration. This model leads to the first optimal algorithm for escape routing. We also extend our model to handle missing pins. We then propose two algorithms for the differential pair escape routing problem. The first one computes the optimal routing for a single differential pair while the second one is able to simultaneously route multiple differential pairs considering both routability and wire length. We then propose a two-stage routing scheme based on the two algorithms. In our routing scheme, the second algorithm is used to generate initial routing and the first algorithm is used to perform rip-up and reroute. Length-constrained routing is another very important problem for PCB routing. Previous length-constrained routers all have assumptions on the routing topology. We propose a routing scheme that is free of any restriction on the routing topology. The novelty of our proposed routing scheme is that we view the length-constrained routing problem as an area assignment problem and use a placement structure to help transform the area assignment problem into a mathematical programming problem. Experimental results show that our routing scheme can handle practical designs that previous routers cannot handle. For designs that they could handle, our router runs much faster. Length-constrained routing requires the escaped nets to have matching ordering along the boundaries of the pin arrays. However, in some practical designs, the net ordering might be mismatched. To address this issue, we propose a preprocessing step to untangle such twisted nets. We also introduce a practical routing style, which we call single-detour routing, to simplify the untangling problem. We discover a necessary and sufficient condition for the existence of single-detour routing solutions and present a dynamic programming based algorithm that optimally solves the problem. By integrating our algorithm into the bus router in a length-constrained router, we show that many routing problems that cannot be solved previously can now be solved with insignificant increase in runtime. The nets on a PCB are usually grouped into buses. Because of the high pin density of the packages, the buses need to be assigned into multiple routing layers. We propose a layer assignment algorithm to assign a set of buses into multiple layers without causing any conflict. Our algorithm guarantees to produce a layer assignment with minimum number of layers. The key idea is to transform the layer assignment problem into a bipartite matching problem. This research result is an improvement over a previous work, which is optimal for only one layer

Optimisation of Mobile Communication Networks - OMCO NET

The mini conference “Optimisation of Mobile Communication Networks” focuses on advanced methods for search and optimisation applied to wireless communication networks. It is sponsored by Research & Enterprise Fund Southampton Solent University. The conference strives to widen knowledge on advanced search methods capable of optimisation of wireless communications networks. The aim is to provide a forum for exchange of recent knowledge, new ideas and trends in this progressive and challenging area. The conference will popularise new successful approaches on resolving hard tasks such as minimisation of transmit power, cooperative and optimal routing

Optimal Network Control in Partially-Controllable Networks

The effectiveness of many optimal network control algorithms (e.g., BackPressure) relies on the premise that all of the nodes are fully controllable. However, these algorithms may yield poor performance in a partially-controllable network where a subset of nodes are uncontrollable and use some unknown policy. Such a partially-controllable model is of increasing importance in real-world networked systems such as overlay-underlay networks. In this paper, we design optimal network control algorithms that can stabilize a partially-controllable network. We first study the scenario where uncontrollable nodes use a queue-agnostic policy, and propose a low-complexity throughput-optimal algorithm, called Tracking-MaxWeight (TMW), which enhances the original MaxWeight algorithm with an explicit learning of the policy used by uncontrollable nodes. Next, we investigate the scenario where uncontrollable nodes use a queue-dependent policy and the problem is formulated as an MDP with unknown queueing dynamics. We propose a new reinforcement learning algorithm, called Truncated Upper Confidence Reinforcement Learning (TUCRL), and prove that TUCRL achieves tunable three-way tradeoffs between throughput, delay and convergence rate

Undesired-Resonance Analysis and Modeling of Differential Signals Due to Narrow Ground Lines Without Stitching Vias

Undesired Resonances on High-Speed Differential Signals Are Studied in This Paper, which is Caused by the Adjacent Narrow Ground Line Without Stitching Vias. Due to Space Limitations in the High-Speed Channel Layouts of Certain Package Applications, the Ground (GND) Line is Often Narrow and Has Insufficient Stitching Vias, Potentially Causing Undesired Resonance in High-Speed Differential Signals. in This Study, These Undesired Resonances Were Investigated using 3D Simulations, revealing that They Can Be Modeled as Parallel-Coupled Half-Wavelength Resonance. the Resonance Frequency of the Parallel-Coupled Half-Wavelength Resonance Structure Can Be Predicted Well using the Formula based on the GND Line Length. Moreover, Three Potential Solutions to Undesired Resonance Are Proposed, Providing a Practical Guide for GND Line Routing in Specific Applications

Rare events statistics of random walks on networks: localization and other dynamical phase transitions

Rare event statistics for random walks on complex networks are investigated using the large deviations formalism. Within this formalism, rare events are realized as typical events in a suitably deformed path-ensemble, and their statistics can be studied in terms of spectral properties of a deformed Markov transition matrix. We observe two different types of phase transition in such systems: (i) rare events which are singled out for sufficiently large values of the deformation parameter may correspond to {\em localized\/} modes of the deformed transition matrix, (ii) "mode-switching transitions" may occur as the deformation parameter is varied. Details depend on the nature of the observable for which the rare event statistics is studied, as well as on the underlying graph ensemble. In the present letter we report on the statistics of the average degree of the nodes visited along a random walk trajectory in Erd\H{o}s-R\'enyi networks. Large deviations rate functions and localization properties are studied numerically. For observables of the type considered here, we also derive an analytical approximation for the Legendre transform of the large-deviations rate function, which is valid in the large connectivity limit. It is found to agree well with simulations.Comment: 5 pages, 3 figure

Early Warning Analysis for Social Diffusion Events

There is considerable interest in developing predictive capabilities for social diffusion processes, for instance to permit early identification of emerging contentious situations, rapid detection of disease outbreaks, or accurate forecasting of the ultimate reach of potentially viral ideas or behaviors. This paper proposes a new approach to this predictive analytics problem, in which analysis of meso-scale network dynamics is leveraged to generate useful predictions for complex social phenomena. We begin by deriving a stochastic hybrid dynamical systems (S-HDS) model for diffusion processes taking place over social networks with realistic topologies; this modeling approach is inspired by recent work in biology demonstrating that S-HDS offer a useful mathematical formalism with which to represent complex, multi-scale biological network dynamics. We then perform formal stochastic reachability analysis with this S-HDS model and conclude that the outcomes of social diffusion processes may depend crucially upon the way the early dynamics of the process interacts with the underlying network's community structure and core-periphery structure. This theoretical finding provides the foundations for developing a machine learning algorithm that enables accurate early warning analysis for social diffusion events. The utility of the warning algorithm, and the power of network-based predictive metrics, are demonstrated through an empirical investigation of the propagation of political memes over social media networks. Additionally, we illustrate the potential of the approach for security informatics applications through case studies involving early warning analysis of large-scale protests events and politically-motivated cyber attacks

Universal optimal broadband photon cloning and entanglement creation in one dimensional atoms

We study an initially inverted three-level atom in the lambda configuration embedded in a waveguide, interacting with a propagating single-photon pulse. Depending on the temporal shape of the pulse, the system behaves either as an optimal universal cloning machine, or as a highly efficient deterministic source of maximally entangled photon pairs. This quantum transistor operates over a wide range of frequencies, and can be implemented with today's solid-state technologies.Comment: 5 pages, 3 figure