Fundamentals and development of PDFAs are described. Spectroscopic data of Pr3+ in a fluoride glass are presented with a view to understanding the performance of PDFA. An amplification mechanism model which explains PDFA performance is established. On the basis of the model pump schemes which efficiently extract the potential in Pr3+-doped fluoride fiber are discussed in order to construct amplifier modules. Gain characteristics of Pr3+-doped fluoride fibers are clarified. Codoping effect on pump wavelength extension is investigated. LD-pumped PDFA construction and performance are described. PDFAs are shown to be attractive device to upgrade the performance of optical systems at 1.3µm. Furthermore future approaches to PDFA research are discussed.

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.

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.

A method to improve the efficiency of dynamic fault imaging (DFI) by fully utilizing the CAD data in the CAD-linked electron beam test system is proposed. In the method, in order to shorten the long acquisition time of the stroboscopic voltage contrast images over the whole area of the chip during the entire test cycle, only the area and phase (time) required for fault tracing are selected by utilizing the CAD data. Furthermore, image processing techniques are combined with the method to improve the efficiency of the DFI. In particular, the signal averaging technique is used in order to improve the signal-to-noise ratio in the stroboscopic images where all voltage information data on the equipotential electrode recognized by the CAD layout data are averaged. This enables us to reduce the acquisition time of images. Moreover, the experimental system is set up so that the image processing can be performed in parallel with the acquisition of the stroboscopic images. The proposed method is applied to part of a 2k-transistor block of a nonpassivated CMOS LSI where a marginal fault is detected. The result shows that the method is an efficient approach to the fully automatic fault diagnosis in the CAD-linked electron beam test system. The proposed method could improve the efficiency of the conventional DFI by a factor of more than 1000.

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.

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.

Results of a DC motor driving test with a power sent by a microwave and extracted with a rectenna array are reported. No significant difference has been observed in the output DC power from the rectenna array between a motor load and a resistive load. Mechanical output could be extracted from the received microwave power with an efficiency of 26%.

This paper presents an algorithm which sums up n binary values on an n m reconfigurable mesh in O(log n/(m log m)1/2) time. This algorithm also yields a corollary which states that n binary values can be summed up on an nlog2n/log log n reconfigurable mesh in constant time.

In this paper, a middle-mapping learning algorithm for cellular associative memories is presented. This algorithm makes full use of the properties of the cellular neural network so that the associative memory has some advantages compared with the memory designed by the ourter product method. It can guarantee each prototype is stored at an equilibrium point. In the practical implementation, it is easy to build up the circuit because the weight matrix presenting the connection between cells is not symmetric. The synchronous updating rule makes its associative speed very fast compared to the Hopfield associative memory.

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.

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.

In this paper, I investigate a property of self-organizing feature map (SOFM) in terms of reference vector density q(x) when probability density function of input signal fed into SOFM is p(x). Difficulty of general analysis on this property is briefly discussed. Then, I employ an assumption (conformal map assumption) to evaluate this property, and it is shown that for equilibrium state, q(x)p(x)s holds. By giving Lyapunov functioin for time evolution of reference vector density q(x) in SOFM, the equilibrium state is proved to be stable in terms of distribution. Comparison of the result with one which is based on different assumption reveals that there is no unique result of a simple form, such as conjectured by Kohonen. However, as there are cases in which these assumptions hold, these results suggest that we can consider a range of the property of SOFM. On the basis of it, we make comparison on this property between SOFM and fundamental adaptive vector quantization algorithm, in terms of the exponent s of the relation q(x)p(x)s. Difference on this property between SOFM and fundamental adaptive vector quantization algorithm, and propriety of mean squared quantization error for a performance measure of SOFM, are discussed.

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.

A convenient method for determining emitter and base resistances from small signal measurements has been developed. This method is based on Neugroschel's method, but the frequency has been varied instead of varying β0. It is demonstrated that the base resistance was successfully extracted. The extracted emitter resistance depended on the collector current because of the difference between the exact gm value and the approximated one, IC/VT. It has also been shown that the proposed method is more robust than the conventional impedance-circle method even when cross-talk occurs.

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.

An improved reflection wave method was described for measurement of complex permittivity of low-loss materials over 100MHz-1GHz range. The residual impedance Zr and stray admittance Ys surrounding the test sample, which terminated the transmission line, were evaluated using sapphire as a reference material. The correction by the obtained Zr and Ys gave accurate values of complex permittivities of alumina and mullite ceramics as 100MHz-1GHz.

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.

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.