On Ultra-Fine Leak Detection of Hermetic Wafer Level Packages

Theoretical and practical ranges of leak rates measurable by the helium mass spectrometer are characterized. The effect of noise due to: 1) background helium present in the spectrometer and 2) desorption of helium that attaches itself to the specimen surface during bombing is quantified experimentally. The results guide a framework to extract the true leak rate from the measured leak rate profile. An optical interferometry based hermeticity measurement technique for ultra-fine leaks is proposed. The setup to implement the technique is described and a preliminary experimental result is reported.     

Semi-orthogonal versus orthogonal wavelet basis sets for solvingintegral equations

The two categories of wavelets, orthogonal and semi-orthogonal, are compared and it is shown that the semi-orthogonal wavelet is best suited for integral equation applications. The Battle-Lemarie orthogonal wavelet and the spline generated semi-orthogonal wavelet are each used to solve for the current distribution on an infinite strip illuminated by a transverse magnetic (TM) plane wave and a straight thin wire illuminated by a normally incident plane wave. The grounds for comparison are accuracy in characterizing the current, matrix sparsity, computation time, and singularity extraction capability. The limitations and advantages of solving integral equations with each of the two wavelet categories are discussed     

On subsurface wireless data acquisition system

Measurement, evaluation, and monitoring of subsurface objects often require wireless data transmission between an embedded sensor and an exterior host system. Such technologies find applications in many areas-medical imaging, space exploration, earth formation evaluation in oilfield industries, for example. This paper describes a complete wireless data acquisition system that includes design of a transceiver unit, as well as communication protocols for data encoding and decoding. Although the application considered in this paper pertains to the oilfield industry, the method is applicable to other areas as well. The transceiver design is highly constrained due to requirements on physical size, mechanical stability, and low-power consumption. The host antenna generally consists of ferrite-backed coils wound on a metallic cylinder. A critical requirement in the design of these coils is to produce a desired spatial variation of the magnetic field in a specified region. A genetic algorithm is used to optimize the location and excitation of each coil. A combination of finite-element method and superposition principle is used to speed up the solution of forward problem. The data measured by an embedded sensor are encoded with seven-bit cyclic redundancy code concatenated with Manchester code for error detection/correction and bit synchronization. Coded data are finally transmitted by binary frequency-shift keying modulation scheme. Numerical and experimental results for magnetic field, signal-to-noise ratio, and data demodulation are presented.     

A robust technique for well-log data inversion

Inverse problems associated with many geophysical measurements are often ill-conditioned, nonunique, and multimodal. Consequently, the gradient-type optimization methods to obtain model parameters become ineffective since the accuracy and convergence of these methods depend highly on initial position and search direction in the parameter space. Evolutionary algorithms which employ direct search global optimization technique are well suited for such problems. In this paper, we apply differential evolution algorithm to invert for the Earth formation properties from measured phase and attenuation by an electromagnetic propagation-type sensor and compare the results with those obtained by conventional method. Results indicate that differential evolution algorithm provides robust results although computationally the method is less efficient.     

Engineering Metal Nanoclusters for Targeted Therapeutics: From Targeting Strategies to Therapeutic Applications

Metal nanoclusters (NCs) have recently attracted great interest in biomedical applications due to their ultrasmall size, good biocompatibility, and unique molecule-like physical and chemical properties. Metal NCs can be rationally designed and integrated with various targeting moieties to achieve unique physicochemical properties and functions. For therapeutic applications, these multifunctional surface-modified NCs can provide distinctive advantages over native metal NCs, such as improved therapeutic effects and reduced side effects. In this review, the design principles of targeting strategies for metal NCs and their composites, including passive and active targeting, and physical and chemical targeting are first discussed. The authors then focus on the recent achievements in the application of metal NCs in targeted therapeutics, including chemotherapy, phototherapy, and radiotherapy. Finally, the authors’ perspectives on the challenges and opportunities of developing metal NCs in targeted therapeutics, further paving their way for potential clinical applications are provided.     

Survey of Deep-Learning Techniques in Big-Data Analytics

Big-data research studies relying upon Deep-learning methods are revitalized the decision-making mechanism in the business sectors and the enterprise domains. The firms’ operational parameters also have the dependency of the Big-data analytics phase, their way of managing the data, and to evolve the outcomes of Big-data implementation by using the Deep-learning algorithms. Deep-learning approaches enhancements in Big-data applications facilitate the decision-making process such as the information-processing to the employees, analytical potentials augmentation, and in the transition of more innovative work. In this DL-approach, the robust-patterns of the data-predictions resulted from the unstructured information by conceptualizing the Decision-making methods. Hence this paper reviewed the impact of the Deep-learning process utilizing the Big-data in the enterprise and Business sectors. Also this study provides a comprehensive survey of all the Deep-learning techniques illustrating the efficiency of Big-Data processing and their impacts of operational parameters. Further it concentrating the data-dimensionality factors and the Big-data complications rectifying by utilizing the DL-algorithms, usage of Machine-learning or deep-learning process for the decision-making mechanism in the Enterprise sectors and business sectors. This research discussed the predictions of the Big-data analytics resulting to the decision parameters within the organisations, and in the management of larger scale of datasets in Big-data analytics processing by utilizing the Deep-learning implementations. The comparative analysis of the reviewed studies has also been described by comparing existing approaches of Deep-learning methodologies in employing Big-data analytics.

     

A clocked high-pass-filter-based offset cancellation technique for high-gain biomedical amplifiers

In this article, a simple offset cancellation technique based on a clocked high-pass filter with extremely low output offset is presented. The configuration uses the on-resistance of a complementary metal oxide semiconductor (CMOS) transmission gate (X-gate) and tunes the lower 3-dB cut-off frequency with a matched pair of floating capacitors. The results compare favourably with the more complex auto-zeroing and chopper stabilisation techniques of offset cancellation in terms of power dissipation, component count and bandwidth, while reporting inferior output noise performance. The design is suitable for use in biomedical amplifier systems for applications such as ENG-recording. The system is simulated in Spectre Cadence 5.1.41 using 0.6 μm CMOS technology and the total block gain is ～83.0 dB while the phase error is <5°. The power consumption is 10.2 mW and the output offset obtained for an input monotone signal of 5 μVpp is 1.28 μV. The input-referred root mean square noise voltage between 1 and 5 kHz is 26.32 nV/√Hz.     

Programmable memory cell using quantum confined Stark effect in multi-quantum well heterojunction bipolar transistor

A three terminal bistable programmable memory cell which can be read either optically or electrically is proposed and demonstrated. The device is based on using Stark effect of the excitonic transitions in a multi-quantum well base region of a heterojunction bipolar transistor. The single device can be flipped (and held) from low transmittance (high voltage) to high transmittance (low voltage) state and vice versa by a varying base current signal.<>     

Waterproof,Breathable, and Antibacterial Self‐Powered e‐Textiles Based on Omniphobic Triboelectric Nanogenerators

Multifunctional electronic textiles (e‐textiles) incorporating miniaturized electronic devices will pave the way toward a new generation of wearable devices and human–machine interfaces. Unfortunately, the development of e‐textiles is subject to critical challenges, such as battery dependence, breathability, satisfactory washability, and compatibility with mass production techniques. This work describes a simple and cost‐effective method to transform conventional garments and textiles into waterproof, breathable, and antibacterial e‐textiles for self‐powered human–machine interfacing. Combining embroidery with the spray‐based deposition of fluoroalkylated organosilanes and highly networked nanoflakes, omniphobic triboelectric nanogenerators (R^F‐TENGs) can be incorporated into any fiber‐based textile to power wearable devices using energy harvested from human motion. R^F‐TENGs are thin, flexible, breathable (air permeability 90.5 mm s^?1), inexpensive to fabricate (<0.04$ cm^?2), and capable of producing a high power density (600 µW cm^?2). E‐textiles based on R^F‐TENGs repel water, stains, and bacterial growth, and show excellent stability under mechanical deformations and remarkable washing durability under standard machine‐washing tests. Moreover, e‐textiles based on R^F‐TENGs are compatible with large‐scale production processes and exhibit high sensitivity to touch, enabling the cost‐effective manufacturing of wearable human–machine interfaces.