Computation of cross-talk alignment by mixed integer linear programming

Noise analysis has been an important and difficult part of design flow of very large-scale integrated (VLSI) systems in many years. In this thesis, the problem of signal alignment resulting in possible maximum peak interconnect coupling noise and propose a variation aware technique for computing combined noise pulse taking into account timing constraints on signal transitions has been discussed. This work shows that the worst noise alignment algorithm can be formulated as mixed integer programming (MLIP) problem both in deterministic window cases and variational window cases. For deterministic window cases, it is assumed that timing windows are given for each aggressor inputs and the victim net is quite. It compares the results from proposed method with the most known and widely used method for computing the worst aggressor alignment - sweeping line algorithm, to verify its correctness and efficiency. For variation window cases, as variations of process and environmental parameters result in variation of start and end points of timing windows, linear approximation is used for approximating effect of process and environmental variations. One of the biggest advantages of MILP formulation of aggressor alignment problem has also been discussed, which is the ability to be easily extended to more complex cases such as non-triangle noise pulses, victim sensitivity window and discontinuous timing windows, this work shows that such extension can be solved by algorithm and does not require development of new algorithms. Therefore, this novel technique can handle noise alignment problem both in deterministic and variational cases and can be easily extended for more complex cases --Abstract, page iii

Principles of Neuromorphic Photonics

In an age overrun with information, the ability to process reams of data has become crucial. The demand for data will continue to grow as smart gadgets multiply and become increasingly integrated into our daily lives. Next-generation industries in artificial intelligence services and high-performance computing are so far supported by microelectronic platforms. These data-intensive enterprises rely on continual improvements in hardware. Their prospects are running up against a stark reality: conventional one-size-fits-all solutions offered by digital electronics can no longer satisfy this need, as Moore's law (exponential hardware scaling), interconnection density, and the von Neumann architecture reach their limits. With its superior speed and reconfigurability, analog photonics can provide some relief to these problems; however, complex applications of analog photonics have remained largely unexplored due to the absence of a robust photonic integration industry. Recently, the landscape for commercially-manufacturable photonic chips has been changing rapidly and now promises to achieve economies of scale previously enjoyed solely by microelectronics. The scientific community has set out to build bridges between the domains of photonic device physics and neural networks, giving rise to the field of \emph{neuromorphic photonics}. This article reviews the recent progress in integrated neuromorphic photonics. We provide an overview of neuromorphic computing, discuss the associated technology (microelectronic and photonic) platforms and compare their metric performance. We discuss photonic neural network approaches and challenges for integrated neuromorphic photonic processors while providing an in-depth description of photonic neurons and a candidate interconnection architecture. We conclude with a future outlook of neuro-inspired photonic processing.Comment: 28 pages, 19 figure

Recognizing Multi-talker Speech with Permutation Invariant Training

In this paper, we propose a novel technique for direct recognition of multiple speech streams given the single channel of mixed speech, without first separating them. Our technique is based on permutation invariant training (PIT) for automatic speech recognition (ASR). In PIT-ASR, we compute the average cross entropy (CE) over all frames in the whole utterance for each possible output-target assignment, pick the one with the minimum CE, and optimize for that assignment. PIT-ASR forces all the frames of the same speaker to be aligned with the same output layer. This strategy elegantly solves the label permutation problem and speaker tracing problem in one shot. Our experiments on artificially mixed AMI data showed that the proposed approach is very promising.Comment: 5 pages, 6 figures, InterSpeech201

Towards Faster Data Transfer by Spoof Plasmonics

With the emergence of complex architectures in modern electronics such as multi-chip modules, the increasing electromagnetic cross-talk in the circuitry causes a serious issue for high-speed, reliable data transfer among the chips. This thesis aims at developing a cross-talk resilient communication technology by utilizing a special form of electromagnetic mode, called spoof surface plasmon polariton for information transfer. The technique is based on the fact that a metal wire with periodic sub-wavelength patterns can support the propagation of confined electromagnetic mode, which can suppress cross-talk noise among the adjacent channels; and thus outperform conventional electrical interconnects in a parallel, high channel density data-bus. My developed model shows that, with 1 THz carrier frequency, the optimal design of cross-talk resilient spoof plasmon data-bus would allow each channel to support as high as 300 Gbps data, the bandwidth density can reach 1 Tbps per millimeter width of data-bus, and the digital pulse modulated carrier can travel more than 5 mm distance on the substrate. I have demonstrated that spoof plasmonic interconnects, comprised of patterned metallic conductors, can simultaneously accommodate electronic TEM mode, which is superior in cross-talk suppression at low-frequencies; and spoof plasmon mode, which is superior at high-frequencies. The research work is divided into two complementary parts: developing a theory for electromagnetic property analysis of spoof plasmon waveguide, and manipulating these properties for high-speed data transfer. Based on the theory developed, I investigated the complex interplay among various figure-of-merits of data transfer in spoof plasmonics, such as bandwidth density, propagation loss, thermal noise, speed of modulation, etc. My developed model predicts that with the availability of 1 THz carrier, the bit-error-rate of spoof plasmon data bus, subject to thermal noise would be $sim10^{-8}$ while the Shannon information capacity of the bus would be $10$ Tbps/mm. The model also predicts that, by proper designing of the modulator, it can be possible to alter the transmission property of the waveguide over one-fifth ($1/5$) of the spoof plasmon band which spans from DC frequency to the frequency of spoof plasmon resonance. To exemplify, if the spoof plasmon resonance is set at $1$ THz, then we can achieve more than $200$ Gbps speed of modulation with a very high extinction ratio, assuming the switching latency of the transistors at our disposal is negligible to the time-resolution of interest. We envision spoof plasmonic interconnects to constitute the next generation communication technology that will be transferring data at hundreds of Gigabit per second (Gbps) speed among different chips on a multi-chip module (MCM) carrier or system-on-chip (SoC) packaging.PHDElectrical EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/163041/1/srjoy_1.pd

Fast and accurate shot noise measurements on atomic-size junctions in the MHz regime

Shot noise measurements on atomic and molecular junctions provide rich information about the quantum transport properties of the junctions and on the inelastic scattering events taking place in the process. Dissipation at the nanoscale, a problem of central interest in nano-electronics, can be studied in its most explicit and simplified form. Here, we describe a measurement technique that permits extending previous noise measurements to a much higher frequency range, and to much higher bias voltage range, while maintaining a high accuracy in noise and conductance. We also demonstrate the advantages of having access to the spectral information for diagnostics.Comment: 8 figure

Time-varying functional connectivity and dynamic neurofeedback with MEG: methods and applications to visual perception

Cognitive function involves the interplay of functionally-separate regions of the human brain. Of critical importance to neuroscience research is to accurately measure the activity and communication between these regions. The MEG imaging modality is well-suited to capturing functional cortical communication due to its high temporal resolution, on the millisecond scale. However, localizing the sources of cortical activity from the sensor measurements is an ill-posed problem, where different solutions trade-off between spatial accuracy, correcting for linear mixing of cortical signals, and computation time. Linear mixing, in particular, affects the reliability of many connectivity measures. We present a MATLAB-based pipeline that we developed to correct for linear mixing and compute time-varying connectivity (phase synchrony, Granger Causality) between cortically-defined regions interfacing with established toolboxes for MEG data processing (Minimum Norm Estimation Toolbox, Brainstorm, Fieldtrip). In Chapter 1, we present a new method for localizing cortical activation while controlling cross-talk on the cortex. In Chapter 2, we apply a nonparametric statistical test for measuring phase locking in the presence of cross-talk. Chapters 3 and 4 describe the application of the pipeline to MEG data collected from subjects performing a visual object motion detection task. Chapter 5 focuses on real-time MEG (rt-MEG) neurofeedback which is the real-time measurement of brain activity and its self-regulation through feedback. Typically neurofeedback modulates directly brain activation for the purpose of training sensory, motor, emotional or cognitive functions. Direct measures, however, are not suited to training dynamic measures of brain activity, such as the speed of switching between tasks, for example. We developed a novel rt-MEG neurofeedback method called state-based neurofeedback, where brain activity states related to subject behavior are decoded in real-time from the MEG sensor measurements. The timing related to maintaining or transitioning between decoded states is then presented as feedback to the subject. In a group of healthy subjects we applied the state-based neurofeedback method for training the time required for switching spatial attention from one side of the visual field to the other (e.g. left side to right side) following a brief presentation of a visual cue. In Chapter 6, we used our pipeline to investigate training-related changes in cortical activation and network connectivity in each subject. Our results suggested that the rt-MEG neurofeedback training resulted in strengthened beta-band connectivity prior to the switch of spatial attention, and strengthened gamma-band connectivity during the switch. There were two goals of this dissertation: First was the development of the MATLAB-based pipeline for computing time-evolving functional connectivity analysis in MEG and its application to visual motion perception. The second goal was the development of a real-time MEG neurofeedback method to train the dynamics of brain states and its application to a group of healthy subjects.2019-11-02T00:00:00

Near-ideal spontaneous photon sources in silicon quantum photonics

While integrated photonics is a robust platform for quantum information processing, architectures for photonic quantum computing place stringent demands on high quality information carriers. Sources of single photons that are highly indistinguishable and pure, that are either near-deterministic or heralded with high efficiency, and that are suitable for mass-manufacture, have been elusive. Here, we demonstrate on-chip photon sources that simultaneously meet each of these requirements. Our photon sources are fabricated in silicon using mature processes, and exploit a novel dual-mode pump-delayed excitation scheme to engineer the emission of spectrally pure photon pairs through intermodal spontaneous four-wave mixing in low-loss spiralled multi-mode waveguides. We simultaneously measure a spectral purity of $0.9904 \pm 0.0006$, a mutual indistinguishably of $0.987 \pm 0.002$, and $>90\%$ intrinsic heralding efficiency. We measure on-chip quantum interference with a visibility of $0.96 \pm 0.02$ between heralded photons from different sources. These results represent a decisive step for scaling quantum information processing in integrated photonics