Memory-based approaches for collaborative filtering identify the similarity between two users by comparing their ratings on a set of items. In the past, the memory-based approach has been shown to suffer from two fundamental problems: data sparsity and difficulty in scalability. Alternatively, the model-based approach has been proposed to alleviate these problems, but this approach tends to limit the range of users. In this paper, we present a novel approach that combines the advantages of these two approaches by introducing a smoothing-based method. In our approach, clusters generated from the training data provide the basis for data smoothing and neighborhood selection. As a result, we provide higher accuracy as well as increased efficiency in recommendations. Empirical studies on two datasets (EachMovie and MovieLens) show that our new proposed approach consistently outperforms other state-of-art collaborative filtering algorithms.
In this paper, the ability of Bragg fiber array to guide optical mode is investigated theoretically. Simulation shows that Bragg fiber array can be looked as a good waveguide to guide inter-fiber modes, which are supported by the external reflection of the 1-D photonic crystal claddings of the Bragg fibers. The mechanism of leakage loss of the inter-fiber is analyzed, showing that the TM01-like mode has the lowest leakage loss and less than 1dB/m leakage loss can be achieve under the wavelength of 10.6μm. As an open waveguide or 2-D microcavity, the Bragg fiber array has wide potential applications including processes of interaction between light and atoms or molecules.
We report the fabrication and absorption measurement of silicon membranes patterned with partially aperiodic nanohole structures. Measurement results agree well with simulations and show enhanced absorption in partially aperiodic structures compared to simple periodic arrays.
The authors report the fabrication and optical characterization of fully suspended, transferrable, and deflectable silicon photonic crystal nanomembranes. Starting with a silicon-on-insulator wafer, the authors used electron beam lithography and inductively coupled plasma reactive ion etching (ICP-RIE) to introduce various photonic crystal patterns in silicon. A membrane containing the photonic crystal patterns was then defined by photolithography combined with ICP-RIE and released from the handle wafer by wet chemical etching. Finally, a free-standing photonic crystal membrane was obtained by a wet transfer and alignment process over a perforated foreign substrate. In the fabricated structures, the authors observed vivid structural colors in dark-field optical images of square lattice photonic crystals and measured a guided resonance mode with a quality factor as high as 5600 in a novel slot-graphite photonic crystal.
Recently nanostructure materials have emerged as a building block for constructing next generation of photovoltaic devices. Nanowire based semiconductor solar cells, among other candidates, have shown potential to produce high efficiency. In a radial pn junction light absorption and carrier collection can be decoupled. Also nanowires can increase choice of materials one can use to fabricate high efficiency tandem solar cells by relaxing the lattice-match constraint. Here we report synthesis of vertical III-V semiconducting nanowire arrays using Selective-Area Metal Organic Chemical Vapor Deposition (SA-MOCVD) technique, which can find application in various optoelectronic devices. We also demonstrate nanosphere lithography (NSL) patterning techniques to obtain ordered pattern for SAMOCVD. Reflection spectrum of nanowires array made by this technique shows excellent light absorption performance without additional anti-reflection coating layer. Thus, we show that highly ordered nanowire structure is 'not needed' to maximize the absorption in vertical nanowire array. Our scalable approach for synthesis of vertical semiconducting nanowire can have application in high throughput and low cost optoelectronic devices including photovoltaic devices.
In addition to lithography process and equipment induced variations, processes like etching, annealing, film deposition and planarization exhibit variations, each having their own intrinsic characteristics and leaving an effect, a 'fingerprint', on the wafers. With ever tighter requirements for CD and overlay, controlling these process induced variations is both increasingly important and increasingly challenging in advanced integrated circuit (IC) manufacturing. For example, the on-product overlay (OPO) requirement for future nodes is approaching <3nm, requiring the allowable budget for process induced variance to become extremely small. Process variance control is seen as an bottleneck to further shrink which drives the need for more sophisticated process control strategies. In this context we developed a novel 'computational process control strategy' which provides the capability of proactive control of each individual wafer with aim to maximize the yield, without introducing a significant impact on metrology requirements, cycle time or productivity. The complexity of the wafer process is approached by characterizing the full wafer stack building a fingerprint library containing key patterning performance parameters like Overlay, Focus, etc. Historical wafer metrology is decomposed into dominant fingerprints using Principal Component Analysis. By associating observed fingerprints with their origin e.g. process steps, tools and variables, we can give an inline assessment of the strength and origin of the fingerprints on every wafer. Once the fingerprint library is established, a wafer specific fingerprint correction recipes can be determined based on its processing history. Data science techniques are used in real-time to ensure that the library is adaptive. To realize this concept, ASML TWINSCAN scanners play a vital role with their on-board full wafer detection and exposure correction capabilities. High density metrology data is created by the scanner for each wafer and on every layer during the lithography steps. This metrology data will be used to obtain the process fingerprints. Also, the per exposure and per wafer correction potential of the scanners will be utilized for improved patterning control. Additionally, the fingerprint library will provide early detection of excursions for inline root cause analysis and process optimization guidance.
We simulate the optical properties of silicon nanowire and nanohole arrays using the transfer matrix method. For optimized parameters, the structures are more absorptive than an optimal single-layer AR-coated thin film.
There is growing interest in how nano- and microstructured materials can be used to achieve high absorption within small material volumes. This area of structural absorption engineering has the ultimate goal of making cheaper, more efficient solar cells. In our work [1-7], we have carried out large-scale electromagnetic simulations to systematically study the structural dependence of optical absorption in periodic nanostructure arrays, such as nanowire [1,2,3,5] and nanohole arrays [2]. We have also investigated the effect of positional randomness and optimal design in aperiodic photovoltaic absorbers [4] and anti-reflecting surface textures [6].
With the development and application of distributed cloud computing, the problem of assigning workflow tasks to computational resources has become more and more prominent. It involves multiple constraints and optimization objectives, and is a typical NP-hard problem. Existing evolutionary algorithms face local optimum and premature convergence problems. Considering these facts, we proposed a multi-objective evolutionary algorithm with elitism strategy (MOEAES) in this paper. To avoid local optimum, MOEAES uses a new crossover operator called Random Sub-Sequence Exchange Crossover (RSSEX), and it introduces a multi-population-based elitism strategy to accelerate the algorithm. Finally, experimental validation is carried out, which shows that MOEAES achieves performance improvement in terms of solution quality and convergence speed comparing to other methods.
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