Zhong Chen

Bio: Zhong Chen is an academic researcher from Nanyang Technological University. The author has contributed to research in topics: Medicine & Chemistry. The author has an hindex of 80, co-authored 1000 publications receiving 28171 citations. Previous affiliations of Zhong Chen include Institute of High Performance Computing Singapore & National Institute of Education.

A Systematic Underfill Selection Methodology for Fine Pitch Cu/Low-k FCBGA Package

Abstract: In this paper, a systematic underfill selection approach has been presented to characterize and identify favorable underfill encapsulants for 21 times 21 mm2 flip chip ball grid array (FCBGA) package with 150 mum interconnect pitch. A total of six evaluation factors of equal ranking weightage were considered in this underfill selection approach. Based on the approach adopted, we have selected the best underfill material suitable for 15 times 15 mm2 FCBGA packages. The target property ranges for underfill materials proposed by the IBM are further being refined. Now, a wider choice of underfill material was found to be applicable for 15 times 15 mm2 FCBGA packages. The new approach has helped to widen the selection criteria for underfill material used in 15 times 15 mm2 FCBGA packages. These findings will assist researchers in having a wider option in underfill selection for future FCBGA packages, which are more challenging.

A Neural Network Method for Nonconvex Optimization and its Application on Parameter Retrieval

Abstract: Parameter retrieval is a typical nonconvex optimization problem in a wide range of research and engineering fields. Classic methods tackle the parameter retrieval problem by feature extraction from the subspace or transform domain. In this paper, we proposed a network-based method to directly solve the nonconvex optimization problem on parameters estimation of complex exponential signals, with no requirement of labeled data. The proposed network has an architecture similar to the Autoencoder network but with the decoder sub-network replaced by a complex exponential signal generator. After training the network to fit the signal parameters to the acquired data, one could obtain the parameters, i.e., frequencies, decay rates, and intensities, and reconstruct the signal. By this work, we show that with a simple application of a lightweight neural network, nonconvex optimization problems like parameter retrieval can be solved efficiently, even without any intricately designed algorithms. We also discuss the robustness of the network-based method by repeated experiments and present the failure cases to indicate the limitations of this method.

Non-synchronization of lattice and carrier temperatures in light-emitting diodes

Abstract: Pulse implementation or switching-off (PISO) of electrical currents has become a common operation in junction-temperature (Tj) measurements for semiconductor devices since 2004. Here we have experimentally discovered a substantial discrepancy between Tj values with, and without, PISO (e.g., 36.8 °C versus 76.5 °C above the ambient temperature at 25.0 °C). Our research indicates that methods associated with PISO are flawed due to non-synchronization of lattice temperatures and carrier temperatures in transient states. To scrutinize this discrepancy, we propose a lattice-inertia thermal anchoring mechanism that (1) explains the cause of this discrepancy, (2) helps to develop a remedy to eliminate this discrepancy by identifying three transient phases, (3) has been applied to establishing an original, accurate, and noninvasive technique for light-emitting diodes to measure Tj in the absence of PISO. Our finding may pave the foundation for LED communities to further establish reliable junction-temperature measurements based on the identified mechanism.

Bleeder Thickness Optimization for Controlling Resin Content in Thick Laminated Composites

Abstract: Thick laminated composites are manufactured commonly by vacuum bagging of fiber-resin mix or prepregs on a suitable mould and, subsequently curing the lay-up at high temperature and pressure in an either autoclave or oven. At these pressures and temperatures, excess resin bleeds out of the lay-up during the initial stages of the curing. The amount of resin bleed is also a function of the bleeder parameters. Bleeder is a porous fibrous media that is laid around stacked lay-up to provide pathway for volatiles as well as absorb and hold the excess resin. Thicker or highly porous bleeders generally absorb higher amount of resin resulting in a resin starved laminate whereas very thin or denser bleeder leads to resin-rich areas within the laminate. It is thus important to select optimum bleeder parameters in order to achieve a desired resin volume fraction and its uniformity in a composite laminate upon curing. This paper details the simulation of the manufacturing of a thick laminated composite, where a significant amount resin is likely to flow out of a curing lay-up, leading to an optimization of bleeder parameters. A coupled, transient FE analysis is conducted that involves not only the heat transfer, resin flow and cure reaction kinetics simulation but also the simulation of the compaction of the wet laminate and the bleeder layers until the laminate is fully cured. Details of an experiment conducted to find compression characteristics of bleeder of varying thickness and the number of layers and related data that was used in the FE analysis are discussed in this paper. It is found that bleeder thickness significantly affects the amount of resin bleeding out from the curing laminate. As a result, the resin volume fraction of the laminate is affected. Case studies carried out to highlight the optimum bleeder thickness for a lay-up, and the method used to decide the thickness and the number of bleeder layers, are presented.

High-resolution localized spatiotemporal encoding correlated spectra under inhomogeneous magnetic fields via asymmetrical gradient encoding/decoding.

Abstract: Applications of conventional localized nuclear magnetic resonance correlated spectroscopy are restrained by long acquisition times and poor performance under inhomogeneous magnetic fields. Here, a method that combines the spatiotemporal encoding technique with the localization technique and implements the encoding and decoding in unison with suitable asymmetrical gradients is proposed to obtain high-resolution localized correlated spectra under inhomogeneous fields in greatly reduced times. Experiments on phantom solutions prove its insensitivity to linear field inhomogeneities along three orthogonal axes. Moreover, this method is applied to adipose study of marrow tissue with resolution improvements. The proposed method may offer promising perspectives for fast analyses of biological tissues. Copyright © 2014 John Wiley & Sons, Ltd.

I and i

Abstract: There is, I think, something ethereal about i —the square root of minus one. I remember first hearing about it at school. It seemed an odd beast at that time—an intruder hovering on the edge of reality. Usually familiarity dulls this sense of the bizarre, but in the case of i it was the reverse: over the years the sense of its surreal nature intensified. It seemed that it was impossible to write mathematics that described the real world in …

Fast parallel algorithms for short-range molecular dynamics

Abstract: Three parallel algorithms for classical molecular dynamics are presented. The first assigns each processor a fixed subset of atoms; the second assigns each a fixed subset of inter-atomic forces to compute; the third assigns each a fixed spatial region. The algorithms are suitable for molecular dynamics models which can be difficult to parallelize efficiently—those with short-range forces where the neighbors of each atom change rapidly. They can be implemented on any distributed-memory parallel machine which allows for message-passing of data between independently executing processors. The algorithms are tested on a standard Lennard-Jones benchmark problem for system sizes ranging from 500 to 100,000,000 atoms on several parallel supercomputers--the nCUBE 2, Intel iPSC/860 and Paragon, and Cray T3D. Comparing the results to the fastest reported vectorized Cray Y-MP and C90 algorithm shows that the current generation of parallel machines is competitive with conventional vector supercomputers even for small problems. For large problems, the spatial algorithm achieves parallel efficiencies of 90% and a 1840-node Intel Paragon performs up to 165 faster than a single Cray C9O processor. Trade-offs between the three algorithms and guidelines for adapting them to more complex molecular dynamics simulations are also discussed.

Principles of Neural Science

Abstract: I have developed "tennis elbow" from lugging this book around the past four weeks, but it is worth the pain, the effort, and the aspirin. It is also worth the (relatively speaking) bargain price. Including appendixes, this book contains 894 pages of text. The entire panorama of the neural sciences is surveyed and examined, and it is comprehensive in its scope, from genomes to social behaviors. The editors explicitly state that the book is designed as "an introductory text for students of biology, behavior, and medicine," but it is hard to imagine any audience, interested in any fragment of neuroscience at any level of sophistication, that would not enjoy this book. The editors have done a masterful job of weaving together the biologic, the behavioral, and the clinical sciences into a single tapestry in which everyone from the molecular biologist to the practicing psychiatrist can find and appreciate his or

Graphitic Carbon Nitride (g-C3N4)-Based Photocatalysts for Artificial Photosynthesis and Environmental Remediation: Are We a Step Closer To Achieving Sustainability?

Abstract: As a fascinating conjugated polymer, graphitic carbon nitride (g-C3N4) has become a new research hotspot and drawn broad interdisciplinary attention as a metal-free and visible-light-responsive photocatalyst in the arena of solar energy conversion and environmental remediation. This is due to its appealing electronic band structure, high physicochemical stability, and “earth-abundant” nature. This critical review summarizes a panorama of the latest progress related to the design and construction of pristine g-C3N4 and g-C3N4-based nanocomposites, including (1) nanoarchitecture design of bare g-C3N4, such as hard and soft templating approaches, supramolecular preorganization assembly, exfoliation, and template-free synthesis routes, (2) functionalization of g-C3N4 at an atomic level (elemental doping) and molecular level (copolymerization), and (3) modification of g-C3N4 with well-matched energy levels of another semiconductor or a metal as a cocatalyst to form heterojunction nanostructures. The constructi...