Optimization of Nb2O5/Ag/Nb2O5 multilayers as transparent composite electrode on flexible substrate with high figure of merit J. Appl. Phys. 112, 103113 (2012) Modulation of external electric field on surface states of topological insulator Bi2Se3 thin films Appl. Phys. Lett. 101, 223109 (2012) Ultra-thin perfect absorber employing a tunable phase change material Appl. Phys. Lett. 101, 221101 (2012) Optical properties of Mg-doped VO2: Absorption measurements and hybrid functional calculations Appl. Phys. Lett. 101, 201902 (2012) Additional information on J. Appl. Phys. Light-matter interactions are of tremendous importance in a wide range of fields from solar energy conversion to photonics. Here the optical dispersion behavior of undoped and 20 mol. % Sm doped ceria thin films, both dense and porous, were evaluated by UV-Vis optical transmission measurements, with the objective of determining both intrinsic and microstructural properties of the films. Films, ranging from 14 to 2300 nm in thickness, were grown on single crystal YSZ(100) and MgO(100) using pulsed laser deposition (both dense and porous films) and chemical vapor deposition (porous films only). The transmittance spectra were analyzed using an in-house developed methodology combining full spectrum fitting and envelope treatment. The index of refraction of ceria was found to fall between 2.65 at a wavelength of 400 nm and 2.25 at 800 nm, typical of literature values, and was relatively unchanged by doping. Reliable determination of film thickness, porosity, and roughness was possible for films with thickness ranging from 500 to 2500 nm. Physically meaningful microstructural parameters were extracted even for films so thin as to show no interference fringes at all. V C 2012 American Institute of Physics.
Quantum plasmonics experiments have on multiple occasions reported the observation of quantum coherence of discrete plasmons, which exhibit remarkable preservation of quantum interference visibility, a seemingly surprising feature for systems mixing light and matter with high Ohmic losses during propagation. However, most experiments to date used essentially weakly-confined plasmons, which experience limited light-matter hybridization, thus limiting the potential for decoherence. Here, we report quantum coherence of plasmons near the surface plasmon polariton (SPP) resonance frequency, where plasmonic dispersion and confinement is much stronger than in previous experiments. We generated polarization-entangled pairs of photons using spontaneous parametric down conversion and transmitted one of the photons through a plasmonic hole array designed to convert incident single photons into highly-dispersive single SPPs. We find the quality of photon entanglement after the plasmonic channel to be unperturbed by the introduction of a highly dispersive plasmonic element. Our findings provide a lower bound of 100 femtoseconds for the pure dephasing time for dispersive plasmons in gold, and show that even in a highly dispersive regime surface plasmons preserve quantum mechanical correlations, making possible harnessing the power of extreme light confinement for integrated quantum photonics.
We apply numerical methods in combination with finite-difference-time-domain (FDTD) simulations to optimize transmission properties of plasmonic mirror color filters using a multi-objective figure of merit over a five-dimensional parameter space by utilizing novel multi-fidelity Gaussian processes approach. We compare these results with conventional derivative-free global search algorithms, such as (single-fidelity) Gaussian Processes optimization scheme, and Particle Swarm Optimization-a commonly used method in nanophotonics community, which is implemented in Lumerical commercial photonics software. We demonstrate the performance of various numerical optimization approaches on several precollected real-world datasets and show that by properly trading off expensive information sources with cheap simulations, one can more effectively optimize the transmission properties with a fixed budget.
Stratigraphic correlation is essential in field evaluation as it provides the necessary tops to compartmentalize the reservoir. It further contributes to other parts of the field development planning cycle such as reservoir modeling, volumetric assessment, production allocation, etc. Traditional approach of manual pairwise correlation is labor-intensive and time-consuming. This research presents a novel automated stratigraphic correlator to create well top and zonation interpretations using supervised machine learning algorithms of Convolutional- and Recurrent-Neural-Networks (CNNs and RNNs). An automated stratigraphic correlator is created that enables stratigraphic well top and zonation interpretations learned from the well logs of a subset of wells with zonation information manually provided by human experts. The method can efficiently learn the patterns and hidden information from the well logs’ sequential data, implicitly capture the domain expertise, and streamline and automate the traditional manual repetitive work. Our method supersedes existing approaches like Multiple Sequence Alignment (MSA) by incorporating domain expertise through tops/zones picked by geologists. A Bidirectional Long Short-Term Memory (BiLSTM) is used to interpret the log data, since deposition by nature is a sequential process and RNNs can intrinsically capture such series. An Inception autoencoder CNN is also applied in this workflow for stratigraphic interpretation. Reliable post-processing is also included using the predicted zone probability logs to quantify the overall confidence score of well zonation, and to correct misinterpretation when necessary using transition frequencies in log data through a linear chain graphical probabilistic model. The methodology is tested on one of the major Middle East oilfields with around 1,500 wells to prove its efficiency and capability. The overall methodology involves data pre-processing, deep learning model training and prediction, and the post-processing of model-predicted results. In this specific workflow, the machine learning targets include both the prediction of zones (multi-class classification/segmentation problem) and the prediction of well tops (edge-detection problem). Thus, a supervised multi-task learning on a single field using CNNs and RNNs is implemented to be able to perform different tasks with the same model. The inputs to the training module include trajectory logs and other measured logs such as gamma-ray, resistivity, neutron density, etc. All inputs are normalized to zero mean and unit standard deviation. For wells with missing log values, the approach can either discard it or perform data imputation to reconstruct the data using different automated algorithms. The machine learning engine uses two different algorithms (BiLSTM and Inception autoencoder CNN), with many other deep learning models tested. The training loss function includes zone categorical cross entropy loss, tops edge detection binary cross entropy loss and L2-norm regularization term. The learning rate is dynamically adjusted during training so that it is reduced when the loss is stalled. The post-processing uses the machine learning predicted zone probability logs to select the zoning sequence that maximizes overall zonation probability and treats it as the confidence score of well zonation. This dramatically helps in constraining the outcome stratigraphic interpretation by geological succession and minimizing the correlation error. The entire workflow has been applied to one major Middle East oilfield with a large number of pre-interpreted well logs, with 60% of the wells used to train the deep learning models, 20% used for validation and the rest are for blind test. Both BiLSTM and Inception autoencoder CNN show close to human-level performance in the blind test dataset. The mean absolute error of well tops interpretation after post-processing is around 3 m throughout all analyzed wells, which provided an accuracy of nearly 90% for the blind test dataset. The classification precision and accuracy also demonstrate close-to-human-level performance in the major zones with sufficient data. It has been noticed that for cases without missing data, Inception autoencoder CNN achieves best performance, while BiLSTM benefits a lot from imputation when missing data exists. The methodology automates and streamlines the originally time-consuming stratigraphic correlation process. It performs better than existing approaches through a well-developed machine learning framework with comprehensive data pre- and post-processing. The resulting stratigraphic correlation proves to be extremely reliable even with a small number of seed wells, and it requires minimal user intervention during the process. Through deep learning techniques such as transfer learning, the proposed methodology can be readily applied to other fields even with limited training data.
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