Design Automation and Test Solutions for Monolithic 3D ICs

Monolithic 3D (M3D) is an emerging heterogeneous integration technology that overcomes the limitations of the conventional through-silicon-via (TSV) and provides significant performance uplift and power reduction. However, the ultra-dense 3D interconnects impose significant challenges during physical design on how to best utilize them. Besides, the unique low-temperature fabrication process of M3D requires dedicated design-for-test mechanisms to verify the reliability of the chip. In this article, we provide an in-depth analysis on these design and test challenges in M3D. We also provide a comprehensive survey of the state-of-the-art solutions presented in the literature. This article encompasses all key steps on M3D physical design, including partitioning, placement, clock routing, and thermal analysis and optimization. In addition, we provide an in-depth analysis of various fault mechanisms, including M3D manufacturing defects, delay faults, and MIV (monolithic inter-tier via) faults. Our design-for-test solutions include test pattern generation for pre/post-bond testing, built-in-self-test, and test access architectures targeting M3D.

Simulation and automation of aluminium panel shot peen forming

We present a methodology for automated forming of metal plates into freeformshapes using shot peening. The methodology is based on a simulation softwarethat computes the peening pattern and simulates the effect of its application.The pattern generation requires preliminary experimental characterizationof the treatment. The treatment is applied by a shot peening robot. The program for the robot is generated automatically according to the peening pattern. We validate the methodology with a series of tests. Namely, we form nine aluminum plates into doubly curved shapes and we also shape model airplane wing skins. The article describes the complete workflow and the experimental results.

Unsupervised functional neurocartography of the Aplysia buccal ganglion

A major limitation of large-scale neuronal recordings is the difficulty in locating the same neuron in different subjects, referred to as the "correspondence" issue. This issue stems, at least in part, from the lack of a unique feature that unequivocally identifies each neuron. One promising approach to this problem is the functional neurocartography framework developed by Frady et al. (2016), in which neurons are identified by a semi-supervised machine learning algorithm using a combination of multiple selected features. Here, the framework was adapted to the buccal ganglia of Aplysia. Multiple features were derived from neuronal activity during motor pattern generation, responses to peripheral nerve stimulation, and the spatial properties of each cell. The feature set was optimized based on its potential usefulness in discriminating neurons from each other, and then used to match putatively homologous neurons across subjects with the functional neurocartography software. A matching method was developed based on a cyclic matching algorithm that allows for unsupervised extraction of groups of neurons, thereby enhancing scalability of the analysis. Cyclic matching was also used to automate the selection of high-quality matches, which allowed for unsupervised implementation of the machine learning algorithm. This study paves the way for investigating the roles of both well-characterized and previously uncharacterized neurons in Aplysia, as well as helps to adapt this framework to other systems.

Nanoimprinted and Anodized Templates for Large-Scale and Low-Cost Nanopatterning

Nanopatterning to fabricate advanced nanostructured materials is a widely employed technology in a broad spectrum of applications going from spintronics and nanoelectronics to nanophotonics. This work reports on an easy route for nanopatterning making use of ordered porous templates with geometries ranging from straight lines to square, triangular or rhombohedral lattices, to be employed for the designed growth of sputtered materials with engineered properties. The procedure is based on large-scale nanoimprinting using patterned low-cost commercial disks, as 1-D grating stamps, followed by a single electrochemical process that allows one to obtain 1-D ordered porous anodic templates. Multiple imprinting steps at different angles enable more complex 2-D patterned templates. Subsequently, sputtering facilitates the growth of ferromagnetic antidot thin films (e.g., from 20 to 100 nm Co thick layers) with designed symmetries. This technique constitutes a non-expensive method for massive mold production and pattern generation avoiding standard lithographical techniques. In addition, it overcomes current challenges of the two-stage electrochemical porous anodic alumina templates: (i) allowing the patterning of large areas with high ordering and/or complex antidot geometries, and (ii) being less-time consuming.

Experimental verification of DSTATCOM for various non-linear load

This paper elaborates a hybrid synchronous reference frame method has been proposed for the distribution static compensator (DSTATCOM) to enhance the compensation performance. This controller is designed in such a way that it generate the reference with respect to the change in load to reject the harmonics. Furthermore, a hysteresis controller is employed for switching pattern generation. Fuzzy logic controller is also implemented in MATLAB/simulink environment here to analyze the total harmonic distortion. Experimental analysis of the DSTATCOM demonstrates the potency and reliability of proposed controller over existing control strategy in the from of total harmonic distortion.

Accelerating Mature Field EOR Evaluation Using Machine Learning Uncertainty Workflows Integrating Subsurface And Economics

Field re-development planning for tertiary recovery projects in mature fields traditionally involves a comprehensive subsurface evaluation circle, including static/dynamic modeling, scenario assessment and candidate selection based on economic models. The aforementioned sequential approach is time-consuming and includes the risk of delaying project maturation. This work introduces a novel approach which integrates subsurface geological and dynamic modeling as well as economics and uses machine learning augmented uncertainty workflows to achieve project acceleration. In the elaborated enhanced oil recovery (EOR) evaluation process, a machine learning assisted approach is used in order to narrow geological and dynamic parameter ranges both for model initialization and subsequent history matching. The resulting posterior parameter distributions are used to create the input models for scenario evaluation under uncertainty. This scenario screening comprises not only an investigation of qualified EOR roll-out areas, but also includes detailed engineering such as well spacing optimization and pattern generation. Eventually, a fully stochastic economic evaluation approach is performed in order to rank and select scenarios for EOR implementation. The presented workflow has been applied successfully for a mature oil field in Central/Eastern Europe with 60+ years of production history. It is shown that by using a fully stochastic approach, integrating subsurface engineering and economic evaluation, a considerable acceleration of up to 75% in project maturation time is achieved. Moreover, the applied workflow stands out due to its flexibility and adaptability based on changes in the project scope. In the course of this case study, a sector roll-out of chemical EOR is elaborated, including a proposal for 27 new well candidates and 17 well conversions, resulting in an incremental oil production of 4.7MM bbl. The key findings were: A workflow is introduced that delivers a fully stochastic economic evaluation while honoring the input and measured data.The delivered scenarios are conditioned to the geological information and the production history in a Bayesian Framework to ensure full consistency of the selected subsurface model advanced to forecasting.The applied process results in substantial time reduction for an EOR re-development project evaluation cycle.

The role of neuronal excitation and inhibition in the pre-Bötzinger complex on the cough reflex in the cat

Brainstem respiratory neuronal network significantly contributes to cough motor pattern generation. Neuronal populations in the pre-Bötzinger complex (PreBötC) represent a substantial component for respiratory rhythmogenesis. We studied the role of PreBötC neuronal excitation and inhibition on mechanically induced tracheobronchial cough in 15 spontaneously breathing, pentobarbital anesthetized adult cats (35 mg/kg i.v. initially). Neuronal excitation by unilateral microinjection of glutamate analog D,L-homocysteic acid resulted in mild reduction of cough abdominal electromyogram (EMG) amplitudes and very limited temporal changes of cough compared to effects on breathing (very high respiratory rate, high amplitude inspiratory bursts with a short inspiratory phase and tonic inspiratory motor component). Mean arterial blood pressure temporarily decreased. Blocking glutamate related neuronal excitation by bilateral microinjections of non-specific glutamate receptor antagonist kynurenic acid reduced cough inspiratory and expiratory EMG amplitude and shortened most cough temporal characteristics similarly to breathing temporal characteristics. Respiratory rate decreased and blood pressure temporarily increased. Limiting active neuronal inhibition by unilateral and bilateral microinjections of GABAA receptor antagonist gabazine resulted in lower cough number, reduced expiratory cough efforts, and prolongation of cough temporal features and breathing phases (with lower respiratory rate). The PreBötC is important for cough motor pattern generation. Excitatory glutamatergic neurotransmission in the PreBötC is involved in control of cough intensity and patterning. GABAA receptor related inhibition in the PreBötC strongly affects breathing and coughing phase durations in the same manner, as well as cough expiratory efforts. In conclusion, differences in effects on cough and breathing are consistent with separate control of these behaviors.