Closed-form crosstalk noise metrics for physical design applications

ABSTRACT In this paper we present efficient closed-form formulas to estimate capacitive coupling-induced crosstalk noise for distributed RC coupling trees. The efficiency of our approach stems from the fact that only the five basic operations are used in the expressions: addition ( ), subtraction ( ), multiplication ( ), division ( ) and square root ( ). The formulas do not require exponent computation or numerical iterations. We have developed closed-form expressions for the peak crosstalk noise amplitude, the peak noise occurring time and the width of the noise waveform. Our approximations are conservative and yet achieve acceptable accuracy. The formulas are simple enough to be used in the inner loops of performance optimization algorithms or as cost functions to guide routers. They capture the influence of coupling direction (near-end and far-end coupling) and coupling location (near-driver and nearreceiver)

Probing context-dependent errors in quantum processors

Gates in error-prone quantum information processors are often modeled using sets of one- and two-qubit process matrices, the standard model of quantum errors. However, the results of quantum circuits on real processors often depend on additional external "context" variables. Such contexts may include the state of a spectator qubit, the time of data collection, or the temperature of control electronics. In this article we demonstrate a suite of simple, widely applicable, and statistically rigorous methods for detecting context dependence in quantum circuit experiments. They can be used on any data that comprise two or more "pools" of measurement results obtained by repeating the same set of quantum circuits in different contexts. These tools may be integrated seamlessly into standard quantum device characterization techniques, like randomized benchmarking or tomography. We experimentally demonstrate these methods by detecting and quantifying crosstalk and drift on the publicly accessible 16-qubit ibmqx3.Comment: 11 pages, 3 figures, code and data available in source file

Feedback-Based Channel Frequency Optimization in Superchannels

Superchannels leverage the flexibility of elastic optical networks and pave the way to higher capacity channels in space division multiplexing (SDM) networks. A superchannel consists of subchannels to which continuous spectral grid slots are assigned. To guarantee superchannel operation, we need to account for soft failures, e.g., laser drifts causing interference between subchannels, wavelength-dependent performance variations, and filter misalignments affecting the edge subchannels. This is achieved by reserving spectral guardband between subchannels or by employing a lower modulation format. We propose a process that dynamically retunes the subchannel transmitter (TX) lasers to compensate for soft failures during operation and optimizes the total capacity or the minimum subchannel quality of transmission (QoT) performance. We use an iterative stochastic subgradient method that at each iteration probes the network and leverages monitoring information, particularly subchannels signal-to-noise ratio (SNR) values, to optimize the TX frequencies. Our results indicate that our proposed method always approaches the optima found with an exhaustive search technique, unsuitable for operating networks, irrespective of the subchannel number, modulation format, roll-off factor, filters bandwidth, and starting frequencies. Considering a four-subchannel superchannel, the proposed method achieves 2.47 dB and 3.73 dB improvements for a typical soft failure of +/- 2 GHz subchannel frequency drifts around the optimum, for the two examined objectives

Architectural Exploration and Design Methodologies of Photonic Interconnection Networks

Photonic technology is becoming an increasingly attractive solution to the problems facing today's electronic chip-scale interconnection networks. Recent progress in silicon photonics research has enabled the demonstration of all the necessary optical building blocks for creating extremely high-bandwidth density and energy-efficient links for on- and off-chip communications. From the feasibility and architecture perspective however, photonics represents a dramatic paradigm shift from traditional electronic network designs due to fundamental differences in how electronics and photonics function and behave. As a result of these differences, new modeling and analysis methods must be employed in order to properly realize a functional photonic chip-scale interconnect design. In this work, we present a methodology for characterizing and modeling fundamental photonic building blocks which can subsequently be combined to form full photonic network architectures. We also describe a set of tools which can be utilized to assess the physical-layer and system-level performance properties of a photonic network. The models and tools are integrated in a novel open-source design and simulation environment called PhoenixSim. Next, we leverage PhoenixSim for the study of chip-scale photonic networks. We examine several photonic networks through the synergistic study of both physical-layer metrics and system-level metrics. This holistic analysis method enables us to provide deeper insight into architecture scalability since it considers insertion loss, crosstalk, and power dissipation. In addition to these novel physical-layer metrics, traditional system-level metrics of bandwidth and latency are also obtained. Lastly, we propose a novel routing architecture known as wavelength-selective spatial routing. This routing architecture is analogous to electronic virtual channels since it enables the transmission of multiple logical optical channels through a single physical plane (i.e. the waveguides). The available wavelength channels are partitioned into separate groups, and each group is routed independently in the network. Each partition is spectrally multiplexed, as opposed to temporally multiplexed in the electronic case. The wavelength-selective spatial routing technique benefits network designers by provider lower contention and increased path diversity

A Low-Power Wireless Multichannel Microsystem for Reliable Neural Recording.

This thesis reports on the development of a reliable, single-chip, multichannel wireless biotelemetry microsystem intended for extracellular neural recording from awake, mobile, and small animal models. The inherently conflicting requirements of low power and reliability are addressed in the proposed microsystem at architectural and circuit levels. Through employing the preliminary microsystems in various in-vivo experiments, the system requirements for reliable neural recording are identified and addressed at architectural level through the analytical tool: signal path co-optimization. The 2.85mm×3.84mm, mixed-signal ASIC integrates a low-noise front-end, programmable digital controller, an RF modulator, and an RF power amplifier (PA) at the ISM band of 433MHz on a single-chip; and is fabricated using a 0.5µm double-poly triple-metal n-well standard CMOS process. The proposed microsystem, incorporating the ASIC, is a 9-channel (8-neural, 1-audio) user programmable reliable wireless neural telemetry microsystem with a weight of 2.2g (including two 1.5V batteries) and size of 2.2×1.1×0.5cm3. The electrical characteristics of this microsystem are extensively characterized via benchtop tests. The transmitter consumes 5mW and has a measured total input referred voltage noise of 4.74µVrms, 6.47µVrms, and 8.27µVrms at transmission distances of 3m, 10m, and 20m, respectively. The measured inter-channel crosstalk is less than 3.5% and battery life is about an hour. To compare the wireless neural telemetry systems, a figure of merit (FoM) is defined as the reciprocal of the power spent on broadcasting one channel over one meter distance. The proposed microsystem’s FoM is an order of magnitude larger compared to all other research and commercial systems. The proposed biotelemetry system has been successfully used in two in-vivo neural recording experiments: i) from a freely roaming South-American cockroach, and ii) from an awake and mobile rat.Ph.D.Electrical EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/91542/1/aborna_1.pd

Physical Layer Aware Optical Networks

This thesis describes novel contributions in the field of physical layer aware optical networks. IP traffic increase and revenue compression in the Telecom industry is putting a lot of pressure on the optical community to develop novel solutions that must both increase total capacity while being cost effective. This requirement is pushing operators towards network disaggregation, where optical network infrastructure is built by mix and match different physical layer technologies from different vendors. In such a novel context, every equipment and transmission technique at the physical layer impacts the overall network behavior. Hence, methods giving quantitative evaluations of individual merit of physical layer equipment at network level are a firm request during network design phases as well as during network lifetime. Therefore, physical layer awareness in network design and operation is fundamental to fairly assess the potentialities, and exploit the capabilities of different technologies. From this perspective, propagation impairments modeling is essential. In this work propagation impairments in transparent optical networks are summarized, with a special focus on nonlinear effects. The Gaussian Noise model is reviewed, then extended for wideband scenarios. To do so, the impact of polarization mode dispersion on nonlinear interference (NLI) generation is assessed for the first time through simulation, showing its negligible impact on NLI generation. Thanks to this result, the Gaussian Noise model is generalized to assess the impact of space and frequency amplitude variations along the fiber, mainly due to stimulated Raman scattering, on NLI generation. The proposed Generalized GN (GGN) model is experimentally validated on a setup with commercial linecards, compared with other modeling options, and an example of application is shown. Then, network-level power optimization strategies are discussed, and the Locally Optimization Global Optimization (LOGO) approach reviewed. After that, a novel framework of analysis for optical networks that leverages detailed propagation impairment modeling called the Statistical Network Assessment Process (SNAP) is presented. SNAP is motivated by the need of having a general framework to assess the impact of different physical layer technologies on network performance, without relying on rigid optimization approaches, that are not well-suited for technology comparison. Several examples of applications of SNAP are given, including comparisons of transceivers, amplifiers and node technologies. SNAP is also used to highlight topological bottlenecks in progressively loaded network scenarios and to derive possible solutions for them. The final work presented in this thesis is related to the implementation of a vendor agnostic quality of transmission estimator for multi-vendor optical networks developed in the context of the Physical Simulation Environment group of the Telecom Infra Project. The implementation of a module based on the GN model is briefly described, then results of a multi-vendor experimental validation performed in collaboration with Microsoft are shown