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.

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.

We propose a large capacity broadband packet switch architecture using multiple optical star couplers and tunable devices whose tuning range is restricted. The proposed switch has the conventional three-stage switch structure. With the use of the generalized knockout principle and tunable lasers arranged in an appropriate manner, the switch becomes an output queueing system that yields the best possible delay/throughput performance. This switch requires minimal hardware at the cost of the increased number of wavelengths.

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.

In this letter, we present a distinct alternative of cross talk formulation of associative memory based on the outer product algorithm extended to the higher order and a performance evaluation in terms of the probability of exact data recall by using this formulation. The significant feature of these formulations is that both cross talk and the probability formulated are explicitly represented as the functional forms of Hamming distance between the memorized keys and the applied input key, and the degree of higher order correlation. Simulation results show that exact data retrieval ability of the associative memory using randomly generated data and keys is in well agreement with our theoretical estimation.

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.

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.

A new image-based diagnostic method is proposed for use with an E-beam tester. The method features a static fault imaging technique and a navigation map for fault tracing. Static Fault imaging with a dc E-beam enables the fast acquisition of images without any additional hardware. Then, guided by the navigation map derived from CAD data, marginal timing faults can be easily pinpointed. A statistical estimation of the average count of static fault images for various LSI circuits shows that the proposed method can diagnose marginal faults by observing less than thirty faulty images and that a faulty area can be localized with up to five times fewer observations than with the guided-probe method. The proposed method was applied to a 19k-gate CMOS-logic LSI circuit and a marginal timing fault was successfully located.

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.

This paper advocates the use of linear objective function in analytic analog placement. The role of linear and quadratic objctive functions in the behavior and results of an analog placement algorithm based on the force directed method is discussed. Experimental results for a MCNC benchmark circuit and another one from text books are shown to demonstrate the effect of a linear and a quadratic objective function on the analog constraint satisfaction and CPU time. By introducing linear objective function to the algorithm, we obtain better placements in terms of analog constraint satisfaction and computation cost than in case of conventional quadratic objective function.

This letter extends the Yule-Walker method to the estimation of ARMA parameters from output measurements corrupted by noise. In the proposed method it is assumed that the noise variance and the input are unknown. An algorithm for the estimation of noise variance is, therefore, given. The use of the variance estimation method proposed here together with the Yule-Walker equations allow the estimation of the parameters of a minimum phase ARMA model based only on noisy measurements of its output. Moreover, using this method it is not necessary to slove a set of nonlinear equations for MA parameter estimation as required in the conventional correlation based methods.

We have aimed at constructing a forward dynamics model (FDM) of the human arm in the form of an artificial neural network while recordings of EMG and movement trajectories. We succeeded in: (1) estimating the joint torques under isometric conditions and (2) estimating trajectories from surface EMG signals in the horizontal plane. The human arm has seven degrees of freedom: the shoulder has three, the elbow has one and the wrist has three. Only two degrees of freedom were considered in the previous work. Moreover, the arm was supported horizontally. So, free movement in 3D space is still a necessity. And for 3D movements or posture control, compensation for gravity has to be considered. In this papre, four joint angles, one at the elbow and three at the shoulder were estimated from surface EMG signals of 12 flexor and extensor muscles during posture control in 3D space.

This paper proposes a new method of extracting feature attentive regions in a learnt multi-layer neural network. We difine a function which calculates the degree of dependence of an output unit on an inpur unit. The value of this function can be used to investigate whether a learnt network detects the feature regions in the training patterns. Three computer simulations are presented: (1) investigation of the basic characteristic of this function; (2) application of our method to a simpie pattern classification task; (3) application of our method to a large scale pattern classfication task.

This article deals with the binary neural network with negative self-feedback connections as a method for solving combinational optimization problems. Although the binary neural network has a high convergence speed, it hardly searches out the optimum solution, because the neuron is selected randomly at each state update. In thie article, an improvement using the negative self-feedback is proposed. First it is shown that the negative self-feedback can make some local minimums be unstable. Second a selection rule is proposed and its property is analyzed in detail. In the binary neural network with negative self-feedback, this selection rule is effective to escape a local minimum. In order to comfirm the effectiveness of this selection rule, some computer simulations are carried out for the N-Queens problem. For N=256, the network is not caught in any local minimum and provides the optimum solution within 2654 steps (about 10 minutes).

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.

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.

The Alopex algorithm is presented as a universal learning algorithm for neural networks. Alopex is a stochastic parallel process which has been previously applied in the theory of perception. It has also been applied to several nonlinear optimization problems such as the Travelling Salesman Problem. It estimates the weight changes by using only a scalar cost function which is measure of global performance. In this paper we describe the use of Alopex algorithm for solving nonlinear learning tasks by multilayer feed-forward networks. Alopex has several advantages such as, ability to escape from local minima, rapid algorithmic computation based on a scalar cost function and synchronous updation of weights. We present the results of computer simulations for several tasks, such as learning of parity, encoder problems and the MONK's problems. The learning performance as well as the generalization capacity of the Alopex algorithm are compared with those of the backpropagation procedure, and it is shown that the Alopex has specific advantages over backpropagation. An important advantage of the Alopex algorithm is its ability to extract information from noisy data. We investigate the efficacy of the algorithm for faster convergence by considering different error functions. We show that an information theoretic error measure shows better convergence characteristics. The algorithm has also been applied to more complex practical problems such as undersea target recognition from sonar returns and adaptive control of dynamical systems and the results are discussed.

In this paper, it is proven that the following three decision problems on validation of protocols with bounded capacity channels are NP-complete. (1) Given a protocol with the channel capacity being 1, determine whether or not there exist deadlocks in the protocol. (2) Given a protocol with the channel capacity being 1, determine whether or not there exist unspecified receptions in the protocol. (3) Given a protocol with the channel capacity being 2, determine whether or not there exist overflows such that the channel capacity is not bounded by 1 in the protocol. These results suggest that, even when all channeles in a protocol are bounded by 1 or 2, protocol validation should be in general interactable. It also clarifies the boundary of computational complexity of protocol validation problems because the channel capacity 0 does not allow protocols to transmit and recieve messages.

The planar point location problem is one of the most fundamental problems in computational geometry and stated as follows： Given a straight line planar graph (subdivision) with n vertices and an arbitary query point Q, determine the region containing Q. Many algorithms have been proposed, and some of them are known to be theoretically optimal (O(log n) search time, O(n) space and O(n log n) preprocessing time). In this paper, we implement several representative algorithms in C, and investigate their practical efficiencies by computational experiments on Voronoi diagrams with 210 - 217 vertices.

This paper introduces a new class of machines called multihead marker finite automata, and investigates how the number of markers affects its accepting power. Let HM{0}(i, j)(NHM{0}(i, j))denote the class of languages over a one-letter alphabet accepted by two-way deterministic (nondeterminstic) i-head finite automata with j markers. We show that HM{0} (i, j) HM{0}(i, j1) and NHM{0}(i, j) NHM{0}(i, j+1) for each i2, j0.