Bing J. Sheu

Bio: Bing J. Sheu is an academic researcher from TSMC. The author has contributed to research in topics: Very-large-scale integration & Artificial neural network. The author has an hindex of 30, co-authored 195 publications receiving 3790 citations. Previous affiliations of Bing J. Sheu include University of Southern California & California Institute of Technology.

BSIM: Berkeley short-channel IGFET model for MOS transistors

Abstract: The Berkeley short-channel IGFET model (BSIM), an accurate and computationally efficient MOS transistor model, and its associated characterization facility for advanced integrated-circuit design are described. Both the strong-inversion and weak-inversion components of the drain current expression are included. In order to speed up the circuit-simulation execution time, the dependence of the drain current on the substrate bias has been modeled with a numerical approximation. This approximation also simplifies the transistor terminal-charge expressions. The charge model was derived from its drain-current counterpart to preserve consistency of device physics. Charge conservation is guaranteed in this model.

Measurement and analysis of charge injection in MOS analog switches

Abstract: The analysis has been extended to the general case including signal-source resistance and capacitance. Universal plots of percentage channel charge injected are presented. Normalized variables are used to facilitate usage of the plots. The effects of gate voltage falling rate, signal-source level, substrate doping, substrate bias, switch dimensions, as well as the source and holding capacitances are included in the plots. A small-geometry switch, slow switching rate, and small source resistance can reduce the charge injection effect. On-chip test circuitry with a unity-gain operational amplifier, which reduces the disturbance imposed by measurement equipment to a minimum, is found to be an excellent monitor of the switch charge injection. The theoretical results agree with the experimental data.

Switch-induced error voltage on a switched capacitor

Abstract: A concise analytical expression for switch-induced error voltage on a switched capacitor is derived from the distributed MOSFET model. The result can be interpreted in terms of a simple lumped equivalent circuit. With this expression the dependence is investigated of the error voltage on process parameters and on switch turnoff rate, source resistance, and other circuit parameters. These results can be used to quickly predict the error voltage. The analytical expression is in close agreement with computer simulations and experiments.

Accurate modeling and parameter extraction for MOS transistors valid up to 10 GHz

Abstract: Accurate modeling and efficient parameter extraction of the small signal equivalent circuit of submicrometer MOS transistors for high-frequency operation are presented The equivalent circuit is based on a quasi-static approximation which was found to be adequate in the gigahertz range if the extrinsic components are properly modeled It includes the complete intrinsic quasi-static MOS model, the series resistances of gate, source, and drain, and a substrate coupling network Direct extraction is performed by Y-parameter analysis on the equivalent circuit in the linear and saturation regions of operation The extracted results are physically meaningful and can be used to "de-embed" the extrinsic effects such as the substrate coupling within the device Good agreement has been obtained between the simulation results of the equivalent circuit and measured data up to 10 GHz

Brain-implantable biomimetic electronics as the next era in neural prosthetics

Abstract: An interdisciplinary multilaboratory effort to develop an implantable neural prosthetic that can coexist and bidirectionally communicate with living brain tissue is described. Although the final achievement of such such a goal is many years in the future, it is proposed that the path to an implantable prosthetic is now definable, allowing the problem to be solved in a rational, incremental manner. Outlined in this report is our collective progress in developing the underlying science and technology that will enable the functions of specific brain damaged regions to be replaced by multichip modules consisting of novel hybrid analog/digital microchips. The component microchips are "neurocomputational" incorporating experimentally based mathematical models of the nonlinear dynamic and adaptive properties of biological neurons and neural networks. The hardware developed to date, although limited in capacity, can perform computations supporting cognitive functions such as pattern recognition, but more generally will support any brain function for which there is sufficient experimental information. To allow the "neurocomputational" multichip module to communicate with existing brain tissue, another novel microcircuitry element has been developed-silicon-based multielectrode arrays that are "neuromorphic," i.e., designed to conform to the region-specific cytoarchitecture of the brain, When the "neurocomputational" and "neuromorphic" components are fully integrated, our vision is that the resulting prosthetic, after intracranial implantation, will receive electrical impulses from targeted subregions of the the brain, process the information using the hardware model of that brain region, and communicate back to the functioning brain. The proposed prosthetic microchips also have been designed with parameters that can be optimized after implantation, allowing each prosthetic to adapt to a particular user/patient.

Design of Analog CMOS Integrated Circuits

Abstract: The CMOS technology area has quickly grown, calling for a new text--and here it is, covering the analysis and design of CMOS integrated circuits that practicing engineers need to master to succeed. Filled with many examples and chapter-ending problems, the book not only describes the thought process behind each circuit topology, but also considers the rationale behind each modification. The analysis and design techniques focus on CMOS circuits but also apply to other IC technologies. Table of contents 1 Introduction to Analog Design 2 Basic MOS Device Physics 3 Single-Stage Amplifiers 4 Differential Amplifiers 5 Passive and Active Current Mirrors 6 Frequency Response of Amplifiers 7 Noise 8 Feedback 9 Operational Amplifiers 10 Stability and Frequency Compensation 11 Bandgap References 12 Introduction to Switched-Capacitor Circuits 13 Nonlinearity and Mismatch 14 Oscillators 15 Phase-Locked Loops 16 Short-Channel Effects and Device Models 17 CMOS Processing Technology 18 Layout and Packaging

Fundamentals of Modern VLSI Devices

Abstract: Learn the basic properties and designs of modern VLSI devices, as well as the factors affecting performance, with this thoroughly updated second edition. The first edition has been widely adopted as a standard textbook in microelectronics in many major US universities and worldwide. The internationally-renowned authors highlight the intricate interdependencies and subtle tradeoffs between various practically important device parameters, and also provide an in-depth discussion of device scaling and scaling limits of CMOS and bipolar devices. Equations and parameters provided are checked continuously against the reality of silicon data, making the book equally useful in practical transistor design and in the classroom. Every chapter has been updated to include the latest developments, such as MOSFET scale length theory, high-field transport model, and SiGe-base bipolar devices.

Introduction to data compression

Abstract: Preface 1 Introduction 1.1 Compression Techniques 1.1.1 Lossless Compression 1.1.2 Lossy Compression 1.1.3 Measures of Performance 1.2 Modeling and Coding 1.3 Organization of This Book 1.4 Summary 1.5 Projects and Problems 2 Mathematical Preliminaries 2.1 Overview 2.2 A Brief Introduction to Information Theory 2.3 Models 2.3.1 Physical Models 2.3.2 Probability Models 2.3.3. Markov Models 2.3.4 Summary 2.5 Projects and Problems 3 Huffman Coding 3.1 Overview 3.2 "Good" Codes 3.3. The Huffman Coding Algorithm 3.3.1 Minimum Variance Huffman Codes 3.3.2 Length of Huffman Codes 3.3.3 Extended Huffman Codes 3.4 Nonbinary Huffman Codes 3.5 Adaptive Huffman Coding 3.5.1 Update Procedure 3.5.2 Encoding Procedure 3.5.3 Decoding Procedure 3.6 Applications of Huffman Coding 3.6.1 Lossless Image Compression 3.6.2 Text Compression 3.6.3 Audio Compression 3.7 Summary 3.8 Projects and Problems 4 Arithmetic Coding 4.1 Overview 4.2 Introduction 4.3 Coding a Sequence 4.3.1 Generating a Tag 4.3.2 Deciphering the Tag 4.4 Generating a Binary Code 4.4.1 Uniqueness and Efficiency of the Arithmetic Code 4.4.2 Algorithm Implementation 4.4.3 Integer Implementation 4.5 Comparison of Huffman and Arithmetic Coding 4.6 Applications 4.6.1 Bi-Level Image Compression-The JBIG Standard 4.6.2 Image Compression 4.7 Summary 4.8 Projects and Problems 5 Dictionary Techniques 5.1 Overview 5.2 Introduction 5.3 Static Dictionary 5.3.1 Diagram Coding 5.4 Adaptive Dictionary 5.4.1 The LZ77 Approach 5.4.2 The LZ78 Approach 5.5 Applications 5.5.1 File Compression-UNIX COMPRESS 5.5.2 Image Compression-the Graphics Interchange Format (GIF) 5.5.3 Compression over Modems-V.42 bis 5.6 Summary 5.7 Projects and Problems 6 Lossless Image Compression 6.1 Overview 6.2 Introduction 6.3 Facsimile Encoding 6.3.1 Run-Length Coding 6.3.2 CCITT Group 3 and 4-Recommendations T.4 and T.6 6.3.3 Comparison of MH, MR, MMR, and JBIG 6.4 Progressive Image Transmission 6.5 Other Image Compression Approaches 6.5.1 Linear Prediction Models 6.5.2 Context Models 6.5.3 Multiresolution Models 6.5.4 Modeling Prediction Errors 6.6 Summary 6.7 Projects and Problems 7 Mathematical Preliminaries 7.1 Overview 7.2 Introduction 7.3 Distortion Criteria 7.3.1 The Human Visual System 7.3.2 Auditory Perception 7.4 Information Theory Revisted 7.4.1 Conditional Entropy 7.4.2 Average Mutual Information 7.4.3 Differential Entropy 7.5 Rate Distortion Theory 7.6 Models 7.6.1 Probability Models 7.6.2 Linear System Models 7.6.3 Physical Models 7.7 Summary 7.8 Projects and Problems 8 Scalar Quantization 8.1 Overview 8.2 Introduction 8.3 The Quantization Problem 8.4 Uniform Quantizer 8.5 Adaptive Quantization 8.5.1 Forward Adaptive Quantization 8.5.2 Backward Adaptive Quantization 8.6 Nonuniform Quantization 8.6.1 pdf-Optimized Quantization 8.6.2 Companded Quantization 8.7 Entropy-Coded Quantization 8.7.1 Entropy Coding of Lloyd-Max Quantizer Outputs 8.7.2 Entropy-Constrained Quantization 8.7.3 High-Rate Optimum Quantization 8.8 Summary 8.9 Projects and Problems 9 Vector Quantization 9.1 Overview 9.2 Introduction 9.3 Advantages of Vector Quantization over Scalar Quantization 9.4 The Linde-Buzo-Gray Algorithm 9.4.1 Initializing the LBG Algorithm 9.4.2 The Empty Cell Problem 9.4.3 Use of LBG for Image Compression 9.5 Tree-Structured Vector Quantizers 9.5.1 Design of Tree-Structured Vector Quantizers 9.6 Structured Vector Quantizers 9.6.1 Pyramid Vector Quantization 9.6.2 Polar and Spherical Vector Quantizers 9.6.3 Lattice Vector Quantizers 9.7 Variations on the Theme 9.7.1 Gain-Shape Vector Quantization 9.7.2 Mean-Removed Vector Quantization 9.7.3 Classified Vector Quantization 9.7.4 Multistage Vector Quantization 9.7.5 Adaptive Vector Quantization 9.8 Summary 9.9 Projects and Problems 10 Differential Encoding 10.1 Overview 10.2 Introduction 10.3 The Basic Algorithm 10.4 Prediction in DPCM 10.5 Adaptive DPCM (ADPCM) 10.5.1 Adaptive Quantization in DPCM 10.5.2 Adaptive Prediction in DPCM 10.6 Delta Modulation 10.6.1 Constant Factor Adaptive Delta Modulation (CFDM) 10.6.2 Continuously Variable Slope Delta Modulation 10.7 Speech Coding 10.7.1 G.726 10.8 Summary 10.9 Projects and Problems 11 Subband Coding 11.1 Overview 11.2 Introduction 11.3 The Frequency Domain and Filtering 11.3.1 Filters 11.4 The Basic Subband Coding Algorithm 11.4.1 Bit Allocation 11.5 Application to Speech Coding-G.722 11.6 Application to Audio Coding-MPEG Audio 11.7 Application to Image Compression 11.7.1 Decomposing an Image 11.7.2 Coding the Subbands 11.8 Wavelets 11.8.1 Families of Wavelets 11.8.2 Wavelets and Image Compression 11.9 Summary 11.10 Projects and Problems 12 Transform Coding 12.1 Overview 12.2 Introduction 12.3 The Transform 12.4 Transforms of Interest 12.4.1 Karhunen-Loeve Transform 12.4.2 Discrete Cosine Transform 12.4.3 Discrete Sine Transform 12.4.4 Discrete Walsh-Hadamard Transform 12.5 Quantization and Coding of Transform Coefficients 12.6 Application to Image Compression-JPEG 12.6.1 The Transform 12.6.2 Quantization 12.6.3 Coding 12.7 Application to Audio Compression 12.8 Summary 12.9 Projects and Problems 13 Analysis/Synthesis Schemes 13.1 Overview 13.2 Introduction 13.3 Speech Compression 13.3.1 The Channel Vocoder 13.3.2 The Linear Predictive Coder (Gov.Std.LPC-10) 13.3.3 Code Excited Linear Prediction (CELP) 13.3.4 Sinusoidal Coders 13.4 Image Compression 13.4.1 Fractal Compression 13.5 Summary 13.6 Projects and Problems 14 Video Compression 14.1 Overview 14.2 Introduction 14.3 Motion Compensation 14.4 Video Signal Representation 14.5 Algorithms for Videoconferencing and Videophones 14.5.1 ITU_T Recommendation H.261 14.5.2 Model-Based Coding 14.6 Asymmetric Applications 14.6.1 The MPEG Video Standard 14.7 Packet Video 14.7.1 ATM Networks 14.7.2 Compression Issues in ATM Networks 14.7.3 Compression Algorithms for Packet Video 14.8 Summary 14.9 Projects and Problems A Probability and Random Processes A.1 Probability A.2 Random Variables A.3 Distribution Functions A.4 Expectation A.5 Types of Distribution A.6 Stochastic Process A.7 Projects and Problems B A Brief Review of Matrix Concepts B.1 A Matrix B.2 Matrix Operations C Codes for Facsimile Encoding D The Root Lattices Bibliography Index

Neural networks for the prediction and forecasting of water resources variables: a review of modelling issues and applications

Abstract: Artificial Neural Networks (ANNs) are being used increasingly to predict and forecast water resources variables. In this paper, the steps that should be followed in the development of such models are outlined. These include the choice of performance criteria, the division and pre-processing of the available data, the determination of appropriate model inputs and network architecture, optimisation of the connection weights (training) and model validation. The options available to modellers at each of these steps are discussed and the issues that should be considered are highlighted. A review of 43 papers dealing with the use of neural network models for the prediction and forecasting of water resources variables is undertaken in terms of the modelling process adopted. In all but two of the papers reviewed, feedforward networks are used. The vast majority of these networks are trained using the backpropagation algorithm. Issues in relation to the optimal division of the available data, data pre-processing and the choice of appropriate model inputs are seldom considered. In addition, the process of choosing appropriate stopping criteria and optimising network geometry and internal network parameters is generally described poorly or carried out inadequately. All of the above factors can result in non-optimal model performance and an inability to draw meaningful comparisons between different models. Future research efforts should be directed towards the development of guidelines which assist with the development of ANN models and the choice of when ANNs should be used in preference to alternative approaches, the assessment of methods for extracting the knowledge that is contained in the connection weights of trained ANNs and the incorporation of uncertainty into ANN models.

Artificial neural networks

Abstract: Artificial neural networks (ANNs) constitute a class of flexible nonlinear models designed to mimic biological neural systems. In this entry, we introduce ANN using familiar econometric terminology and provide an overview of ANN modeling approach and its implementation methods. † Correspondence: Chung-Ming Kuan, Institute of Economics, Academia Sinica, 128 Academia Road, Sec. 2, Taipei 115, Taiwan; ckuan@econ.sinica.edu.tw. †† I would like to express my sincere gratitude to the editor, Professor Steven Durlauf, for his patience and constructive comments on early drafts of this entry. I also thank Shih-Hsun Hsu and Yu-Lieh Huang for very helpful suggestions. The remaining errors are all mine.