MATLAB/Simulink is the de facto standard tool for the model-based development (MBD) of control software for automotive systems. A Simulink model developed in MBD for real automotive systems involves complex computation as well as tens of thousands of blocks. In this paper, we focus on decision coverage (DC), condition coverage (CC) and modified condition/decision coverage (MC/DC) criteria, and propose a Monte-Carlo test suite generation method for large and complex Simulink models. In the method, a candidate test case is generated by assigning random values to the parameters of signal templates with specific waveforms. We try to find contributable candidates in a plausible and understandable search space, specified by a set of templates. We implemented the method as a tool, and our experimental evaluation showed that the tool was able to generate test suites for industrial implementation models with higher coverages and shorter execution times than Simulink Design Verifier. Additionally, the tool includes a fast coverage measurement engine, which demonstrated better performance than Simulink Coverage in our experiments.

We numerically simulated electrical properties, i.e., the resistance and Coulomb blockade threshold, of randomly-placed conductive nanoparticles. In simulation, tunnel junctions were assumed to be formed between neighboring particle-particle and particle-electrode connections. On a plane of triangle 100×100 grids, three electrodes, the drain, source, and gate, were defined. After random placements of conductive particles, the connection between the drain and source electrodes were evaluated with keeping the gate electrode disconnected. The resistance was obtained by use of a SPICE-like simulator, whereas the Coulomb blockade threshold was determined from the current-voltage characteristics simulated using a Monte-Carlo simulator. Strong linear correlation between the resistance and threshold voltage was confirmed, which agreed with results for uniform one-dimensional arrays.

Circuits utilizing advanced process technologies have to correctly account for device parameter variation to optimize its performance. In this paper, analytical formulas for evaluating path delay variation of Multi-Threshold CMOS (MTCMOS) circuits are proposed. The proposed formulas express path delay and its variation as functions of process parameters that are determined by fabrication technology (threshold voltage, carrier mobility, etc.) and the circuit parameters that are determined by circuit structure (equivalent load capacitance and the concurrently switching gates). Two procedures to obtain the circuit parameter sets necessary in the calculation of the proposed formulas are also defined. With the proposed formulas, calculation time of a path delay variation becomes three orders faster than that of Monte-Carlo simulation. The proposed formulas are suitably applied for efficient design of MTCMOS circuits considering process variation.

We research on an importance sampling (IS) simulation to estimate a low error probability of turbo codes. The simulation time reduction in IS depends on another probability density function (p.d.f.) called simulation p.d.f. The previous IS simulation method can not evaluate the error probability on the low SNR and waterfall region. We derive the optimal simulation p.d.f. which gives the perfect estimator. A new simulation p.d.f. design, which is related to the optimal one, is proposed to overcome the problem of the previous IS method. The proposed IS simulation can evaluate all possible error patterns. Finally, some computer simulations show that the proposed method can evaluate the error probability on the low SNR, waterfall, and error floor regions. At the evaluation of the BER of 10-7, the simulation time of the proposed method is about 1/350 times as short as that of the Monte-Carlo simulation. When the BER is less than 710-8, the proposed method requires shorter simulation time than the conventional IS method.

Parallel concatenated convolutional codes, turbo codes, are very attractive scheme at a point of view of an error probability performance. An bit error rate (BER) evaluation for turbo codes is done by a uniform interleaver bound calculation and/or a computer simulation. The former is calculated under the assumption of uniform interleaver, and is only effective for an BER evaluation with a pseudo random interleaver. The latter dose not have any interleaver restrictions. However, for a very low BER evaluation, it takes enormous simulation time. In this paper, a new error probability evaluation method for turbo codes is proposed. It is based on the error event simulation method. For each evaluation for the predetermined error sequence, importance sampling, which is one of the fast simulation methods, is applied. To prove the effectiveness of the proposed method, numerical examples are shown. The proposed method well approximates the BER at the error floor region. Under the same accuracy, the IS estimation time at BER = 10-7 is reduced to 1/6358 of the ordinary Monte-Carlo simulation time.

A method for estimating the bit-error rate (BER) of turbo codes called the Monte Carlo distance spectrum method is proposed. Testing this method shows that the estimated BER curves closely approximate the results of a Monte Carlo simulation.

Coefficient-based test (CBT) is introduced for detecting parametric faults in analog circuits. The method uses pseudo Monte-Carlo simulation and system identification tools to determine whether a given circuit under test (CUT) is faulty.

We present a physics-based circuit simulator employing the Monte Carlo (MC) particle technique, which serves as a bridge between the small-device physics and the circuit designs. Two different geometries of GaAs-MESFET's are modeled and analyzed by the simulator. The Y-parameters of the devices are extracted from the transient currents, and then translated into the S-parameters. The cut-off frequency (fT) is estimated from the Y-parameters. The minimum noise figure (Fmin) is also estimated by evaluating the fluctuation in the stationary current. The device, having the n+-region placed just at the drain side of the gate, exhibits the better performances in both fT and Fmin. The analysis on the equivalent circuit (EC) elements reveals that its better performances are mainly due to the reduced gate-source capacitance (Cgs) and the increased transconductance (gm0), which result from the shortened effective gate length (Lg) caused by the termination of the depletion region at the gate edge. The termination of the depletion region, however, causes the increase of the electric field, which results in the higher heat generation rate near the gate edge. It is proven that the physics-based circuit simulator developed here is fully effective to see the inside of the small-device and to model it for the millimeter-wave circuit design.

This letter presents a Monte-Carlo FDTD technique to determine the scattered field from a perfectly conducting fractal surface from which the useful information on the incoherent pattern tendency could be observed. A one-dimensional fractal surface was generated by the bandlimited Weierstrass function. In order to verify the numerical results by this technique, these results are compared with those of Kirchhoff approximations, which show a good match between them. To investigate the incoherent pattern tendency involved, the dependence of the fitting curve slope on the different D and is discussed for the bistatic and back scattering case, respectively.

We propose bit error rate (BER) evaluation methods for a trellis coded modulation (TCM) scheme over a Rayleigh fading channel by using importance sampling (IS). The simulation probability density function for AWGN and Rayleigh fading is separately designed. For efficient simulation of a system model with finite interleaver, frequency of the generation of fading sequences is reduced. The proposed method gives a good BER estimates over a Rayleigh fading channel.

The high accuracy which is necessary for modern process simulation often requires the use of Monte-Carlo ion implantation simulation methods with the disadvantage of very long simulation times especially for three-dimensional applications. In this work a new method for an accurate and CPU time efficient three-dimensional simulation of ion implantation is suggested. The approach is based on a combination of the algorithmic capabilities of a fast analytical and the Monte-Carlo simulation method.

Estimating the deadline of a real-time task is a necessary prerequisite to the applications that have strict timing constraints, such as real-time systems design. This paper shows how Monte-Carlo simulation can be used as a space-efficient way of analyzing Timed Petri nets to predict whether the system specified can satisfy its real-time deadlines. For the purpose, Extended Timed Petri Net (XTPN), an extension of conventional Timed Petri net, and its execution rule, using Monte-Carlo technique, are newly defined. A simple simulation scheme with less memory space is presented as a way of estimating the deadline of a real-time task modeled in XTPN. And the comparison between the analytical and simulation results is given. The problem addressed here is to find the probabilities of meeting given deadlines.

A large scale simulation for polymer chains in good solvent is performed. The implementation technique for efficient parallel execution, optimization, and load-balancing are discussed on this practical application. Finally, a simple performance model is proposed.

The evaluation of a error probability of a trellis-coded modulation scheme by an ordinary Monte-Carlo simulation method is almost impossible since the excessive simulation time is required to evaluate it. The reduction of the number of simulation runs required is achieved by an importance sampling method, which is one of the variance reduction simulation methods. The reduction of it is attained by the modification of the probability density function, which makes errors more frequent. The error event simulation method, which evaluates the error probability of finite important error events, cannot avoid a truncation error. It is the fatal problem to evaluate the precision of the simulation result. The reason of it is how to design the simulation probability density function. We propose a evaluation method and the design methods of the simulation conditional probability density function. The proposed method simulates any error event starting at the fixed time, and the estimator of it has not the truncation error. The proposed design method approximate the optimum simulation conditional probability density function. By using the proposed method for an additive non-Gaussian noise case, the simulation time of the most effective case of the proposed method is less than 1/5600 of the ordinary Monte-Carlo method at the bit error rate of 10-6 under the condition of the same accuracy if the overhead of the selection of the error events is excluded. The simulation time of the same bit error rate is about 1/96 even if we take the overhead for the importance sampling method into account.

When bit error probability of a trellis-coded modulation (TCM) scheme becomes very small, it is almost impossible to evaluate it by an ordinary Monte-Carlo simulation method. Importance sampling is a technique of reducing the number of simulation samples required. The reduction is attained by modifying the noise to produce more errors. The low error rate can be effectively estimated by applying importance sampling. Each simulation run simulates a single error event, and importance sampling is used to make the error events more frequent. The previous design method of the probability density function in importance sampling is not suitable for the TCM scheme on an additive non-Gaussian noise channel. The main problem is how to design the probability density function of the noise used in the simulation. We propose a new design method of the simulation probability density function related to the Bhattacharyya bound. It is reduced to the same simulation probability density function of the old method when the noise is additive white Gaussian. By using the proposed method for an additive non-Gaussian noise, the reduction of simulation time is about 1/170 at bit error rate of 106 if the overhead of the calculation of the Bhattacharyya bound is ignored. Under the same condition, the reduction of the simulation time by the proposed method is 1/65 of the ordinary Monte-Carlo method even if we take the overhead for importance sampling into account.

Impact ionization () in two n+-n--n+ device structures is investigated. Data obtained from self-consistent Monte-Carlo (SCMC) simulations of the devices is used to show that the average energy () of only those high energy electrons contributing to is an appropriate variable for the modeling of . A transport model allowing one to calculate is derived from the Boltzmann transport equation (BTE) and calibrated by the SCMC simulation results. The values of and the coefficient, αii, predicted by the proposed model are in good agreement with the Monte-Carlo data.