This paper discusses Mixed Signal LSI technology with embedded power transistors. Trends in Mixed Signal LSI technology are explained at first. Mixed signal LSI technology has proceeded with the help of fine fabrication technology and SOI technology. The BEOL transistor is a new development, which uses InGaZnO (IGZO) as its TFT channel material. The BEOL transistor is one future device which enables 3D IC and chip shrinking technology.

This paper proposes a novel and an efficient method termed hypersphere sampling to estimate the circuit yield of low-failure probability with a large number of variable sources. Importance sampling using a mean-shift Gaussian mixture distribution as an alternative distribution is used for yield estimation. Further, the proposed method is used to determine the shift locations of the Gaussian distributions. This method involves the bisection of cones whose bases are part of the hyperspheres, in order to locate probabilistically important regions of failure; the determination of these regions accelerates the convergence speed of importance sampling. Clustering of the failure samples determines the required number of Gaussian distributions. Successful static random access memory (SRAM) yield estimations of 6- to 24-dimensional problems are presented. The number of Monte Carlo trials has been reduced by 2-5 orders of magnitude as compared to conventional Monte Carlo simulation methods.

Many kinds of data can be represented as a network or graph. It is crucial to infer the latent structure underlying such a network and to predict unobserved links in the network. Mixed Membership Stochastic Blockmodel (MMSB) is a promising model for network data. Latent variables and unknown parameters in MMSB have been estimated through Bayesian inference with the entire network; however, it is important to estimate them online for evolving networks. In this paper, we first develop online inference methods for MMSB through sequential Monte Carlo methods, also known as particle filters. We then extend them for time-evolving networks, taking into account the temporal dependency of the network structure. We demonstrate through experiments that the time-dependent particle filter outperformed several baselines in terms of prediction performance in an online condition.

Many controllers are implemented on digital platforms as periodic control tasks. But, in embedded systems, an amount of resources are limited and the reduction of resource utilization of the control task is an important issue. Recently, much attention has been paid to a self-triggered controller, which updates control inputs aperiodically. A control task by which the self-triggered controller is implemented skips the release of jobs if the degradation of control performances by the skipping can be allowed. Each job computes not only the updated control inputs but also the next update instant and the control task is in the sleep state until the instant. Thus the resource utilization is reduced. In this paper, we consider self-triggered predictive control (stPC) of mixed logical dynamical (MLD) systems. We introduce a binary variable which determines whether the control inputs are updated or not. Then, we formulate an stPC problem of mixed logical dynamical systems, where activation costs are time-dependent to represent the preference of activations of the control task. Both the control inputs and the next update instant are computed by solving a mixed integer programming problem. The proposed stPC can reduce the number of updates with guaranteeing stability of the controlled system.

The long-term degradation due to aging such as NBTI (Negative Bias Temperature Instability) is a hot issue in the current circuit design using nanometer process technologies, since it causes a delay fault in the field. In order to resolve the problem, we must estimate delay variation caused by long-term degradation in design stage, but over estimation must be avoided so as to make timing design easier. If we can treat such a variation statistically, and if we treat it together with delay variations due to process variability, then we can reduce over margin in timing design. Moreover, such a statistical static timing analyzer treating process variability and long-term degradation together will help us to select an appropriate set of paths for which field testing are conducted to detect delay faults. In this paper, we propose a new delay model with a half triangular distribution, which is introduced for handling a random factor with unknown distribution such as long term degradation. Then, we show an algorithm for finding the statistical maximum, which is one of key operations in statistical static timing analysis. We also show a few experimental results demonstrating the effect of the proposed model and algorithm.

This paper proposes a robust bilateral filter which can handle mixed Gaussian and impulsive noise by hybridizing the conventional bilateral filter and the switching median filter. The effectiveness of the proposed method is verified in comparison with other conventional methods by some experiments using the natural digital images.

This paper proposes a new double-edge-triggered implicitly level-converting flip-flop, suitable for a low-power and low-voltage design. The design employs a sense amplifier architecture to reduce the delay and power consumption. Experimentally, when implemented with a 130-nm, single-Vt and 0.84V VDD process, it achieves 64% power-delay product (PDP) improvement, and moreover, 78% PDP improvement when implemented with a mixed-Vt technology, as compared to that of the classic double-edge-triggered flip-flop design.

The linearity analysis of a passive mixer is presented. The distortion mechanism caused by switching operation of a MOS transistor is elucidated from the static and dynamic analysis of passive mixers. Furthermore, the maximum input and output level to keep linear operation and its required bias conditions are expressed by simple equations. The maximum linear output amplitude of the passive mixer is determined only by the local signal amplitude and it does not depend on input and output impedance. The calculated linearity performances agree well with simulated and measured results.

Large-scale introduction of renewable energy such as photovoltaic energy and wind is a big motivation for renovating conventional grid systems. To be independent from existing power grids and to use renewable energy as much as possible, a decentralized energy network is proposed as a new grid system. The decentralized energy network is placed among houses to connect them with each other, and each house has a PV panel and a battery. A contribution of this paper is a network topology and battery size exploration for the decentralized energy network in order to make effective use of renewable energy. The proposed method for exploring the decentralized energy network design is inspired by the design methodology of VLSI systems, especially design space exploration in system-level design. The proposed method is based on mixed integer programming (MIP) base power flow optimization, and it was evaluated for all design instances. Experimental results show that the decentralized energy network has the following features. 1) The energy loss and energy purchased due to power shortage were not affected by each battery size but largely affected by the sum of all battery sizes in the network, and 2) the network topology did not largely affect the energy loss and the purchased energy. These results will become a useful guide to designing an optimal decentralized energy network for each region.

The response of differential pairs against low-frequency substrate voltage variation is captured in a combined transistor and substrate network models. The model generation is regularized for variation of transistor geometries including channel sizes, fingering and folding, and the placements of guard bands. The expansion of the models for full-chip substrate noise analysis is also discussed. The substrate sensitivity of differential pairs is evaluated through on-chip substrate coupling measurements in a 90 nm CMOS technology with more than 64 different geometries and operating conditions. The trends and strengths of substrate sensitivity are shown to be well consistent between simulation and measurements.

Substrate coupling of radio frequency (RF) components is represented by equivalent circuits unifying a resistive mesh network with lumped capacitors in connection with the backside of device models. Two-port S-parameter test structures are used to characterize the strength of substrate coupling of resistors, capacitors, inductors, and MOSFETs in a 65 nm CMOS technology with different geometries and dimensions. The consistency is finely demonstrated between simulation with the equivalent circuits and measurements of the test structures, with the deviation of typically less than 3 dB for passive and 6 dB for active components, in the transmission properties for the frequency range of interest up to 8 GHz.

This work proposes an exponential computation with low-computational complexity and applies this technique to the expectation-maximization (EM) algorithm for Gaussian mixture model (GMM). For certain machine-learning techniques, such as the EM algorithm for the GMM, fast and low-cost implementations are preferred over high precision ones. Since the exponential function is frequently used in machine-learning algorithms, this work proposes reducing computational complexity by transforming the function into powers of two and introducing a look-up table. Moreover, to improve efficiency the look-up table is scaled. To verify the validity of the proposed technique, this work obtains simulation results for the EM algorithm used for parameter estimation and evaluates the performances of the results in terms of the mean absolute error and computational time. This work compares our proposed method against the Taylor expansion and the exp( ) function in a standard C library, and shows that the computational time of the EM algorithm is reduced while maintaining comparable precision in the estimation results.

This paper presents a new technique to smooth speech feature vectors for text-independent speaker verification using an adaptive band-pass IIR filer. The filter is designed by considering the probability density of modulation-frequency components of an M-dimensional feature vector. Each dimension of the feature vector is processed and filtered separately. Initial filter parameters, low-cut-off and high-cut-off frequencies, are first determined by the global mean of the probability densities computed from all feature vectors of a given speech utterance. Then, the cut-off frequencies are adapted over time, i.e. every frame vector, in both low-frequency and high-frequency bands based also on the global mean and the standard deviation of feature vectors. The filtered feature vectors are used in a SVM-GMM Supervector speaker verification system. The NIST Speaker Recognition Evaluation 2006 (SRE06) core-test is used in evaluation. Experimental results show that the proposed technique clearly outperforms a baseline system using a conventional RelAtive SpecTrA (RASTA) filter.

The recent increase in mobile data traffic has resulted in service quality problems. Although an economic approach to control congestion can be achieved by pricing, the current pricing schedule of mobile data services instead causes smartphone users to create more traffic. We establish a pricing model based on the distribution of demand types among heterogeneous users to improve the current tariff structure; our method mixes usage-based and fixed-fee pricing schemes. The results derived from the application of this model to survey data on willingness-to-pay for mobile data service demonstrate that the provider can decrease the amount of data traffic and increase the expected revenue by lowering the price for a unit of data and raising the fixed-fee level for unlimited service. The model also explains the changing weight of usage-based and fixed-fee pricing schemes by considering shifts in the type distribution through service evolution and proposes pricing strategies for future communications services.

We propose a method of the precise frequency tuning in millimeter wave (MMW) generation using a Mach-Zehnder-modulator-based flat comb generator (MZ-FCG). The MZ-FCG generates a flat comb signal where the comb spacing is exactly the same as the frequency of a radio-frequency signal driving the MZ-FCG. Two modes are extracted from the comb signal by using optical filters. One of them was modulated by a phase modulator, creating precisely frequency-controllable sidebands. In the experiment, typical phase modulation was used. By photomixing of the extracted two modes using a high-speed photodiode, MMW signals with precisely frequency-controllable sidebands are generated. By changing the modulation frequency, the frequency of MMW signals can be continuously tuned. In this scheme, there are two methods for the frequency tuning of MMW signals; one is a coarse adjustment which corresponds to the comb spacing, and the other is fine tuning by the phase-modulation. It was demonstrated that the intensity fluctuation of the upper sideband of the modulated MMW signal was less than 1 dB, and the frequency fluctuation was less than the measurement resolution (300 Hz).

This paper presents efficient secure auction protocols for first price auction and second price auction. Previous auction protocols are based on a generally secure multi-party protocol called mix-and-match protocol based on plaintext equality tests. However, the time complexity of the plaintext equality tests is large, although the mix-and-match protocol can securely calculate any logical circuits. The proposed protocols reduce the number of times the plaintext equality tests is used by replacing them with the Boneh-Goh-Nissim encryption, which enables calculation of 2-DNF of encrypted data.

MPEG spatial audio object coding (SAOC) is a new audio coding standard which efficiently represents various audio objects as a down-mix signal and spatial parameters. MPEG SAOC has a backward compatibility with existing playback systems for the down-mix signal. If a mastering signal is used for providing CD-like sound quality instead of the down-mix signal, an output signal decoded with the mastering signal may be easily degraded due to the difference between the down-mix and the mastering signals. To successfully use the mastering signal in MPEG SAOC, the difference between two signals should be eliminated. As a simple mastering signal processing, we propose a mastering signal processing using the mastering down-mix gain (MDG) which is similar to the arbitrary down-mix gain of MPEG Surround. Also, we propose an enhanced mastering signal processing using the MDG bias in order to reduce quantization errors of the MDG. Experimental results show that the proposed schemes can improve sound quality of the output signal decoded with the mastering signal. Especially, the enhanced method shows better performance than the simple method in the aspects of the quantization errors and the sound quality.

High-performance FFT processor is indispensable for real-time OFDM communication systems. This paper presents a CORDIC based design of variable-length FFT processor which can perform various FFT lengths of 64/128/256/512/1024/2048/4096/8192-point. The proposed FFT processor employs memory based architecture in which mixed radix 4/2 algorithm, pipelined CORDIC, and conflict-free parallel memory access scheme are exploited. Besides, the CORDIC rotation angles are generated internally based on the transform of butterfly counter, which eliminates the need of ROM making it memory-efficient. The proposed architecture has a lower hardware complexity because it is ROM-free and with no dedicated complex multiplier. We implemented the proposed FFT processor and verified it on FPGA development platform. Additionally, the processor is also synthesized in 0.18 µm technology, the core area of the processor is 3.47 mm2 and the maximum operating frequency can be up to 500 MHz. The proposed FFT processor is better trade off performance and hardware overhead, and it can meet the speed requirement of most modern OFDM system, such as IEEE 802.11n, WiMax, 3GPP-LTE and DVB-T/H.

In this paper, we present a speech enhancement technique based on the ambient noise classification that incorporates the Gaussian mixture model (GMM). The principal parameters of the statistical model-based speech enhancement algorithm such as the weighting parameter in the decision-directed (DD) method and the long-term smoothing parameter of the noise estimation, are set according to the classified context to ensure best performance under each noise. For real-time context awareness, the noise classification is performed on a frame-by-frame basis using the GMM with the soft decision framework. The speech absence probability (SAP) is used in detecting the speech absence periods and updating the likelihood of the GMM.

In this paper, the research advances in silicon based millimeter wave and THz ICs in the State Key Laboratory of Millimeter Waves is reviewed, which consists of millimeter wave amplifiers, mixers, oscillators at Q, V and W and D band based on CMOS technology, and several research approaches of THz passive ICs including cavity and filter structures using SIW-like (Substrate Integrated Waveguide-like) guided wave structures based on CMOS and MEMs process. The design and performance of these components and devices are presented.