Recent developments and case studies regarding VLSI device chip failure analysis are reviewed. The key failure analysis techniques reviewed include EMMS (emission microscopy), OBIC (optical beam induced current), LCM (liquid crystal method), EBP (electron beam probing), and FIB (focused ion beam method). Further, future possibilities in failure analysis, and some promising new tools are introduced.

This paper describes a 32-bit fully asynchronous microprocessor, with 4-stage pipeline based on a RISC-like architecture. Issues relevant to the processor such as design of self-timed datapath, asynchronous controller and interconnection circuits are discussed. Simulation results are included using parameters extracted from layout, which showed about the 300 MIPS processing speed and used 71,000 transistors with 0.5 µm CMOS technology.

When intermetal oxide film which contains much water deposited on MOSFET, degradation of hot carrier characteristics is enhanced. This mechanism is considered to be as follows. During the annealing process water is desorbed from the intermetal oxide. The desorbed water reaches the MOSFET and eventually hydrogens terminate silicon dangling bonds in the gate oxide. This paper describes a new approach which uses ESR to analyze this mechanism. The ESR measurement of number of the silicon dangling bonds in undoped polysilicon lying under the intermetal oxide shows that water diffuses from intermetal oxide to MOSFET during the annealing process. The water diffusion is blocked by introduction between the polysilicon and the intermetal oxides of P-SiN layer or CVD SiO2 damaged by implantation.

An analysis of the circuit for dead angle compensation in the dc-to-dc converter controlled by a magnetic amplifier is presented. This circuit suppresses the dead angle so that the core loss may be reduced without spoiling the current surge suppression characteristics of the magnetic amplifier. The analysis is given by modeling the magnetization characteristics of the core containing the saturation inductance and the reverse recovery of the diode. As a result, the control characteristics of the converter with the compensation circuit are expressed analytically and a limit of compensation is derived theoretically.

Persistent data structures, introduced by Sarnak and Tarjan, have been found especially useful in designing geometric algorithms. In this paper, we present a persistent form of binary-binary search tree, and then apply this data structure to solve various geometric searching problems, such as, three dimensional ray-shooting, hidden surface removal, polygonal point enclosure searching and so on. In all applications, we are able to either improve existing bounds or establish new bounds.

Graph parameters such as connectivity and diameter have been studied extensively due to their intrinsic importance in graph theory, combinatorics and their relations to (and applications in) fault tolerance and transmission delay in communications networks. The advent of VLSI technology and fiber optics material science has enabled us to design massively parallel processing computer systems and fast and complicated communications networks. All these systems increase their reliability by studying (among other) the existence of two (or more) disjoint paths connecting any two nodes. This paper addresses these issues by studying the width and length of containers in graphs and networks. In particular, the notions of w-distance and w-diameter on a graph are defined and studied which generalize both concepts of connectivity and diameter. Thses notions are also considered in finite groups. Other closely related parameters will be explored in the contexts of fault tolerance and routing. Known results are surveyed and open problems are offered for further investigation.

Many previous works state that a multiple Sidelobe canceller (MSLC) with two auxiliary antennas is successful in suppressing two interference signals received simultaneously by sidelobes of a main antenna. In this paper, we show that the MSLC does not always guarantee such capability in three dimensional applications where the incident direction of interference signals is defined by two angles (elevation and azimuth). We show the singularity of the autocorrelation matrix for the auxiliary channel signals induces the degradation of the capability by analyzing characteristics of MSLC's in three dimensional applications from the view point of the eigenvalue problem. To overcome this singularity, we propose a novel MSLC controlling the placement of auxiliary antennas by means of switching over three antennas arranged triangularly. Some simulations are conducted to show the effectiveness of the proposed MSLC.

Analysis of satellite images requires classificatio of image objects. Since different categories may have almost the same brightness or feature in high dimensional remote sensing data, many object categories overlap with each other. How to segment the object categories accurately is still an open question. It is widely recognized that the assumptions required by many classification methods (maximum likelihood estimation, etc.) are suspect for textural features based on image pixel brightness. We propose an image feature based neural network approach for the segmentation of AVHRR images. The learning algoriothm is a modified backpropagation with gain and weight decay, since feedforward networks using the backpropagation algorithm have been generally successful and enjoy wide popularity. Destructive algorithms that adapt the neural architecture during the training have been developed. The classification accuracy of 100% is reached for a validation data set. Classification result is compared with that of Kohonen's LVQ and basic backpropagation algorithm based pixel-by-pixel method. Visual investigation of the result images shows that our method can not only distinguish the categories with similar signatures very well, but also is robustic to noise.

This paper proposes an approach for approximately realizing nonlinear mappings of interval vectors by interval neural networks. Interval neural networks in this paper are characterized by interval weights and interval biases. This means that the weights and biases are given by intervals instead of real numbers. First, an architecture of interval neural networks is proposed for dealing with interval input vectors. Interval neural networks with the proposed architecture map interval input vectors to interval output vectors by interval arithmetic. Some characteristic features of the nonlinear mappings realized by the interval neural networks are described. Next, a learning algorithm is derived. In the derived learning algorithm, training data are the pairs of interval input vectors and interval target vectors. Last, using a numerical example, the proposed approach is illustrated and compared with other approaches based on the standard back-propagation neural networks with real number weights.

A new regularization cost function for generalization in real-valued function learning is proposed. This cost function is derived from the maximum likelihood method using a modified sample distribution, and consists of a sum of square errors and a stabilizer which is a function of integrated square derivatives. Each of the regularization parameters which gives the minimum estimation error can be obtained uniquely and non-empirically. The parameters are not constants and change in value during learning. Numerical simulation shows that this cost function predicts the true error accurately and is effective in neural network learning.

We propose a fast computation method of stochastic relaxation for the continuous-valued Markov random field (MRF) whose energy function is represented in the quadratic form. In the case of regularization in visual information processing, the probability density function of a state transition can be transformed to a Gaussian function, therefore, the probablistic state transition is realized with Gaussian random numbers whose mean value and variance are calculated based on the condition of the input data and the neighborhood. Early visual information processing can be represented with a coupled MRF model which consists of continuity and discontinuity processes. Each of the continuity or discontinuity processes represents a visual property, which is like an intensity pattern, or a discontinuity of the continuity process. Since most of the energy function for early visual information processing can be represented by the quadratic form in the continuity process, the probability density of local computation variables in the continuity process is equivalent to the Gaussian function. If we use this characteristic, it is not necessary for the discrimination function computation to calculate the summation of the probabilities corresponding to all possible states, therefore, the computation load for the state transition is drastically decreased. Furthermore, if the continuous-valued discontinuity process is introduced, the MRF model can directly represent the strength of discontinuity. Moreover, the discrimination function of this energy function in the discontinuity process, which is linear, can also be calculated without probability summation. In this paper, a fast method for calculating the state transition probability for the continuous-valued MRF on the visual informtion processing is theoretically explained. Next, initial condition dependency, computation time and dependency on the statistical estimation of the condition are investigated in comparison with conventional methods using the examples of the data restoration for a corrupted square wave and a corrupted one-dimensional slice of a natural image.

A network system is proposed for segmenting and extracting multiple moving objects in 2D images. The system uses an interconnected neural network in which grouping factors, such as edge proximity, smoothness of edge orientatio, and smoothness of velocity perpendicular to an edge, are embedded. The system groups edges so that the network energy may be minimized, i.e. edges may be organized into perceptually plausible configuration. Experimantal results are provided to indicate the performance and noise robustness of the system in extracting objects in synthetic images.

In this paper, we develop a unified synthesizing approach for the cloning templates of Cellular Neural Networks (CNNs). In particular, we shall consider the case when the signal processing problem is complex, and a multilayered CNN with time-variant templates is necessary. The method originates from the existence of correspondence between the cloning templates of Cellular Neural Network and its discrete counterpart, Discrete-Time Cellular Neural Network (DTCNN), in solving a prescribed image processing problem when time-variant templates are involved. Thus, one can start with calculating the cloning templates from DTCNN, and then translating the cloning templates to those for CNN operations. As a result, the mathematical tools being used in the synthesis of Discrete-time Cellular Neural Network can also be applied to the analog type Cellular Neural Network. This inevitably helps to simplify the design problem of CNN for signal processing. Examples akin to contour drawing and parallel thinning are shown to illustrate the merits of our proposed method.

The number of nodes in a hidden layer of a feed-forward layered network reflects an optimality condition of the network in coding a function. It also affects the computation time and the ability of the network to generalize. When an arbitrary number of hidden nodes is used in designing the network, redundancy of hidden nodes often can be seen. In this paper, a method of reducing hidden nodes is proposed on the condition that a reduced network maintains the performances of the original network within an accepted level of tolerance. This method can be applied to estimate the performances of a network with fewer hidden nodes. The estimated performances indicate the lower bounds of the actual performances of the network. Experiments were performed using the Fisher's IRIS data, a set of SONAR data, and the XOR data for classification. The results suggest that sufficient number of hidden nodes, fewer than the original number, can be estimated by the proposed method.

This paper proposes a new method to determine the shape of a surface by learning the mapping between three image irradiances observed under illumination from three lighting directions and the corresponding surface gradient. The method uses Phong reflectance function to describe specular reflectance. Lambertian reflectance is included as a special case. A neural network is constructed to estimate the values of reflectance parameters and the object surface gradient distribution under the assumption that the values of reflectance parameters are not known in advance. The method reconstructs the surface gradient distribution after determining the values of reflectance parameters of a test object using two step neural network which consists of one to extract two gradient parameters from three image irradiances and its inverse one. The effectiveness of this proposed neural network is confirmed by computer simulations and by experiment with a real object.

Optical amplifier structures suitable for a 622Mbit/s repeater in an optical communication system containing one in-line amplifier have been investigated. Two wavelengths of 1.533µm and 1.549µm are considered for two cases, i.e., single-channel transmission and two-channel wavelength division multiplexing transmission. The basic amplifier structure is of a two-stage type where forward pumped and backward pumped erbium-doped fibers are connected with each other through intermediate optical filters and an optical isolator. First, the effect of the intermediate optical filters was clarified in optical gain and bit error rate characteristics. Then, the erbium-doped fiber length was optimized on the basis of the allowable optical loss of the optical system which was operated at a bit error rate of 10-9. As a result, the appropriate length of the forward pumped erbium-doped fiber was found to be about 20m for both cases of single-channel and two-channel wavelength multiplexing amplifiers. With the designed amplifier used in the system, the calculated allowable optical line loss was more than 90dB for both the cases.

Recently, the throughput performances of ARQ's have been analyzed over a Markov error channel. It has been shown that given a round-trip-delay, the throughput of the Stop-and-Wait ARQ is dependent only on the overall average packet-error probability. In this paper, we exactly analyze the Stop-and-Wait ARQ scheme under the condition that the channel is slotted and packet errors occur according to a two-state Markov chain which is characterized by the decay factor. The distribution of packet delay time and the channel usage factor are obtained. From the analytical results and numerical examples, it is shown that for a given round-trip-delay, the average packet delay time and the channel utilization factor depend on both the overall average packet-error probability and the decay factor characterizing the two-state Markov chain. Furthermore, the decay factor gives different influence on the average delay time and the channel usage factor depending on whether the round-trip-delay is even slots or not.

The timing jitter reductions with differently shaped optical bandpass filters are discussed and the transmission distance achievable against the timing jitter is evaluated using optical bandpass filters in several tens of Gb/s soliton transmission. Experimental confirmation of timing jitter reduction with optical bandpass filters is demonstrated in 10Gb/s optical soliton recirculating loop experiments by measuring the timing jitter and the bit error rates.

This paper studies the performance of a dialogue communication system which consists of two stations over a half-duplex line. When a station seizes the right to send its packets, it can consecutively transmits k packets. We analyze the transmission time of a message and the throughput performances of Stop-and-Wait, Go-back-N and Selective-Repeat protocols for the half-duplex line transmission system. Based on the analytical and numerical results, we clarify the influences of the switching and the thinking times, which exist in half-duplex line system, on the throughput performance, and give the optimal k which makes the throughput to become maximum. It is observed that the throughput performances are greatly influenced not only by the switching and thinking times but also by the average message length.