The generation and design of a stationary Markov signal are discussed as an inverse problem, in which one looks for a transition probability when a stationary probability distribution is given. This paper presents a new solution to the inverse problem, which makes it possible to design and generate a Markov random signal with arbitrary probability distribution and an exponential correlation function. Several computer results are illustrated in figures.

This paper uses both network analysis and experiments to confirm that the neural network learning algorithm that minimizes output variation (BPV) provides much more robustness than back-propagation (BP) or BP with noise-modified training samples (BPN). Network analysis clarifies the relationship between sample displacement and what and how the network learns. Sample displacement generates variation in the output of the output units in the output layer. The output variation model introduces two types of deformation error, both of which modify the mean square error. We propose a new error which combines the two types of deformation error. The network analysis using this new error considers that BPV learns two types of training samples where the modification is either towards or away from the category mean, which is defined as the center of sample distribution. The magnitude of modification depends on the position of the training sample in the sample distribution and the degree of leaning completion. The conclusions is that BPV learns samples modified towards to the category mean more stronger than those modified away from the category mean, namely it achieves nonuniform learning. Another conclusion is that BPN learns from uniformly modified samples. The conjecture that BPV is much more robust than the other two algorithms is made. Experiments that evaluate robustness are performed from two kinds of viewpoints: overall robustness and specific robustness. Benchmark studies using distorted handprinted Kanji character patterns examine overall robustness and two specifically modified samples (noise-modified samples and directionally-modified samples) examine specific robustness. Both sets of studies confirm the superiority of BPV and the accuracy of the conjecture.

We describe 2.5Gb/s 4 channel WDM transmission over 1060km using 18 EDFAs. Gain bandwidth narrowing in concatenated EDFAs has been successfully suppressed using unsaturated EDFAs and a 1.53µm ASE rejection filter.

Following a discussion of various testing methods used in the electron beam (EB) test system, new waveform-based and image-based approaches in the CAD-linked electron beam (EB) test system are proposed. A waveform-based automatic tracing algorithm of the transistor-level performance faults is first discussed. Then, the method to improve the efficiency of an image-based method called dynamic fault imaging (DFI) by fully utilizing the CAD data is described. Third, the VLSI development cost is analyzed by using the fault models that make possible to take into consideration the effect of new testing technologies such as EB testing and focused ion beam (FIB) microfabrication. Finally, the future prospects are discussed.

The origin of and a method of enhancing the Optical Beam Induced Resistance Change (OBIRCH) signal for defect observation in VLSI metal interconnections is discussed based on a numerical analysis of three-dimensional thermal conduction and experimental results. The numerical analysis shows that the OBIRCH signal originates from a slight increase in the resistance of the metal line caused by laser beam heating and that its effect is influenced by the temperature of the metal layer. Both simulations and experimental results suggest that cooling the sample is preferable to detect the OBIRCH signal. The decrease in the total resistance of the metal line without any change in the amount of the resistance increase under laser illumination is found to be the main cause of the OBIRCH signal enhancement under low temperature measurement.

The mechanism for data retention failure of EPROM has been investigated by the Optical Beam Induced Current(OBIC) technique. It was found that the data of failure cells were changed from '1' to '0' during read-mode by laser irradiation by OBIC. The data in good cells was not changed. This result suggests the effective barrier height between Si and SiO2 is being lowered. In addition, the cross section technique revealed that gate electrode and gate oxide were exposed due to lack of dielectric layers. This defect seemed to be the cause of the barrier height lowering. The OBIC technique not only gives the failure location but a detailed information of the failure mechanism. We found that OBIC technique is a very powerful tool for the analysis of EPROM failure mechanisms. The usefulness of the Emission Micro Scope (EMS) technique is also discussed.

A procedure for preparing a cross-sectional transmission electron microscopy (TEM) micrograph of a specific area is outlined. A specific area in a specimen has been very difficult to observe with TEM, because a particular small area cannot be preselected in the conventional specimen preparation technique using mechanical polishing, dimpling and ion milling. The technique in this paper uses a focused ion beam (FIB) to fabricate a cross-sectional specimen at a desired area. The applications of this specimen preparation technique are illustrated for investigations of particles in the process of fabricating devices and degraded aluminum/aluminum vias. The specimen preparation technique using FIB is useful for observing a specific area. This technique is also useful for shortening the time of specimen preparation and observing wide areas of LSI devices.

Operation characteristics of tapered-waveguide traveling wave semiconductor laser amplifier (TTW-SLA) are calculated in terms of quasi adiabatic single mode propagation, signal gain and saturation output power, device efficiency(the efficiency of conversion between the electrical and amplified optical power), and amplified spontaneous emission (ASE) power, and their dependences on the shape of the taper are compared for linear, quadratic, Gaussian and exponential functions, It was found that in the allowed quasi adiabatic single mode propagation condition, linear and Gaussian TTW-SLA have higher saturation output power property, while the exponential TTW-SLA has higher device efficiency property and lower ASE noise of about 0.1 times that of a broad type TW-SLA.

In this paper, we show that the neural network can approximate the chaotic behavior in nonlinear dynamical system by experimental study. Chaotic neural activities have been reported in many respects including neural network field. On the contrary, can the neural network learn the chaotic behavior? There have been explored the neural network architecture for predicting successive elements of a sequence. Also there have been several studies related to learning algorithms for general recurrent neural networks. But they often require complicated procedure in time calculation. We use simple standard backpropagation for a kind of simple recurrent neural network. Two types of chaotic system, differential equation and difference equation, are examined to compare characteristics. In the experiments, Lorenz equation is used as an example of differential equation. One-dimensional logistic equation and Henon equation are used as examples of difference equation. As a result, we show the approximation ability of chaotic dynamics in difference equation, which is logistic equation and Henon equation, by neural network. To indicate the chaotic state, we use Lyapunov exponent which represents chaotic activity.

Recently, the back propagation method, which is one of the algorithms for learning neural networks, has been widely applied to various fields because of its excellent characteristics. But it has drawbacks, for example, slowness of learning speed, the possibility of falling into a local minimum and the necessity of adjusting a learning constant in every application. In this article we propose an algorithm which overcomes some of the drawbacks of the back propagation by using an improved genetic algorithm.

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.

New detection method of passivation defect was studied. The method was the Cu decoration method without bias (bias-free Cu decoration). As the result of comparison with conventional method, it was found that a bias-free Cu decoration method was effective, sensitive and simple. In this method, the difference of humidity resistance induced by poor passivation coverage could be evaluated.

A problem which often arises in computer vision is that of matching corresponding points of images. In the case of object recognition, for example, the computer compares new images to templates from a library of known objects. A common way to perform this comparison is to extract feature points from the images and compare these points with the template points. Another common example is the case of motion detection, where feature points of a video image are compared to those of the previous frame. Note that in both of these example, the point correspondence is complicated by the fact that the point sets are not only randomly ordered but have also been distorted by an unknown transformation and having quite different coordinates. In the case of object recognition, there exists a transformation from the object being viewed, to its projection onto the camera's imaging plane, while in the motion detection case, this transformation represents the motion (translation and rotation) of the ofject. If the parameters of the transformation are completely unknow, then all n! permutations must be compared (n : number of feature points). For each permutation, the ensuing transformation is computed using the least-squared projection method. The exponentially large computation required for this is prohibitive. A neural computational method is propopsed to solve these combinatorial problems. This method obtains the best correspondence matching and also finds the associated transform parameters. The method was applied to two dimensional point correspondence and three-to-two dimensional correspondence. Finally, this connectionist approach extends readily to a Boltzmann machine implementation. This implementation is desirable when the transformation is unknown, as it is less sensitive to local minima regardless of initial conditions.

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.

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.

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.

This report describes 4-2 compressors composed of Complementary Pass-Transistor Logic (CPL). We will show that circuit designs of the 4-2 compressors can be optimized for high speed and small size using only exclusive-OR's and multiplexers. According to a circuit simulation with 0.8µm CMOS device parameters, the maximum propagation delay and the average power consumption per unit adder are 1.32 ns and 11.6 pJ, respectively.

Recently, many researches on associative memories have been made a lot of neural network models have been proposed. Bidirectional Associative Memory (BAM) is one of them. The BAM uses Hebbian learning. However, unless the traning vectors are orthogonal, Hebbian learning does not guarantee the recall of all training pairs. Namely, the BAM which is trained by Hebbian learning suffers from low memory capacity. To improve the storage capacity of the BAM, Pseudo-Relaxation Learning Algorithm for BAM (PRLAB) has been proposed. However, PRLAB needs long learning epochs because of random initial weights. In this paper, we propose Quick Learning for BAM which greatly reduces learning epochs and guarantees the recall of all training pairs. In the proposed algorithm, the BAM is trained by Hebbian learning in the first stage and then trained by PRLAB. Owing to the use of Hebbian learning in the first stage, the weights are much closer to the solution space than the initial weights chosen randomly. As a result, the proposed algorithm can reduce the learning epocks. The features of the proposed algorithm are: 1) It requires much less learning epochs. 2) It guarantees the recall of all training pairs. 3) It is robust for noisy inputs. 4) The memory capacity is much larger than conventional BAM. In addition, we made clear several important chracteristics of the conventional and the proposed algorithms such as noise reduction characteristics, storage capacity and the finding of an index which relates to the noise reduction.

The transient behavior in the fractal admittance acting as a non-integer-rank differential/integral operator, Y(s) ∝ sa with -1a1 and a0, is examined from the point of view of memory effects by employing the distributed-relaxation-time model. The internal state of the diode is found to be represented by the current spectrum i(λ, t) with respect to the carrier relaxation rate λ, leading to a general formulation of the long-time-tail memory behavior characteristic of the operator. One-to-one corrsepondence is found among the input voltage in the past ν(-t), the short-circuit current isc(t) and the initial current spectrum i(λ, 0) within the framework of the Laplace-type integral transformation and its inverse, assuring that each response retains in principle the entire information on the corresponding input, such as the functional form, the magnitude, the onset time, and so forth. The current and voltage responses are exemplified for various single-pulse voltage inputs. The responses to the pulse-train inputs corresponding to different ASCII codes are found to be properly discriminated between one another, showing the potentials of the present memory effects.

In this paper we analize the performance of Trellis Coded Modulation (TCM) schemes with coherent detection operating in a frequency flat, mobile Rayleigh fading environment, and with different knowledge levels on both the amplitude and phase fading processes (the latter is not assumed as usual to be ideally tracked), or Channel State Information (CSI). For example, whereas ideal CSI means that both the amplitude and phase fading characteristics are perfectly known by the receiver, other situations that are treated consider perfect knowledge of the amplitude (or phase) with complete disregard of the phase (or amplitude), as well as non concern on any of them. Since these are extreme cases, intermediate situations can be also defined to get extended bounds based on Chernoff which allow the phase errors, in either form of constant phase shifts or randomly distributed phase jitter, to be included in the upper bounds attainable by transfer function methods, and are applicable to multiphase/level signaling schemes. We found that when both fading characteristics are considered, the availability of CSI enhances significatively the performance. Furthermore, for non constant envelope schemes with non ideal CSI and for constant envelope schemes with phase errors, an asymmetry property of the pairwise error probability is identified. Theoretical and simulation results are shown in support of the analysis.