Non-Homogeneous Poisson Process (NHPP) is a standard framework in the field of software reliability analysis. The core of NHPP consists in determining the Mean Value Function (MVF) of cumulative error number at a specific time slot. However, practice shows the difficulty in finding a general model to fit all sorts of fault data. A certain model is only sensitive to the specific object(s). Modeling failure MVF for NHPP still faces a number of challenges such as making reasonable explanation of assumption, determining fault detection rate per error, fault modification efficiency, error introduction rate, etc. In this research, we propose a novel Software Reliability Growth Model (SRGM) by leveraging generalized imperfect debugging NHPP framework. We first provide physical explanations for assumptions on error modification, error introduction and fault detection rate per error. Meanwhile, we generate a typical constraint relationship between the total error introduction rate and change rate of residual errors. We also describe the fault detection rate per error with the form of exponential decay function, and use error reduction factor to form the new model. Furthermore, we make extensive discussions based on our proposed model. The experimental results confirm that our proposed model is effective on fault fitting and prediction, especially excellent on short-term prediction.

As typical examples of rotational rate sensors, microelectromechanical system (MEMS) gyroscopes have been widely applied as inertial devices in various fields, including national defense, aerospace, healthcare, etc. This review systematically summarizes research advancements in MEMS gyroscope structural forms and processing technologies, which are evaluated through performance indices. The review encompasses several areas. First, it outlines the modelling principles and processes of gyroscopes on the basis of the Coriolis force and resonance, establishing a theoretical foundation for MEMS gyroscope development. Second, it introduces and analyzes the latest research advances in MEMS gyroscope structures and corresponding processing technologies. On the basis of published research advances, this review categorically discusses multidisciplinary technology properties, statistical results, the existence of errors, and compensation methods. Additionally, it identifies challenges in MEMS gyroscope technologies through classification analysis.

Under the circumstance of rapidly fluctuating temperatures, the measurement of electromagnetic parameter of medium becomes notably more challenging due to alterations in the morphology, state of matter, and dimensions of materials resulting from ablation over a brief period. In this article, we focus on exploring a novel approach related to the measurement of time-varying complex permittivity and thickness of material under its several temperature states, with a particular emphasis on the impact of high-temperature ablation on the parameter variation of material. Initially, by measuring three typical reflection coefficients based on built single-antenna measurement system, we propose a novel measurement approach through adoption of a special cancellation technique to mitigate adverse signals that contain inherent reflection information and unknown stray signals. Subsequently, we build an explicit constraint relationship based on the definition of a generalized reflection coefficient, through which the optimization solutions of material under test (MUT) can be obtained in a well-founded manner. Furthermore, utilizing inverse chirp Z transform (ICZT) and quantum-behaved particle swarm optimization (QPSO) as techniques for obtaining the time-domain initial solutions and frequency-domain optimal solutions, respectively, we perform interval estimation of the obtained solutions to ensure their certain confidence by adopting the bootstrap strategy. The calculation results based on simulation and application of actual test data demonstrate that our proposed measurement technique and data-processing methods are effective and reliable for the measurement of time-varying complex permittivity and thickness of materials.

In this paper, the transient breakdown voltage (TrBV) of a silicon-on-insulator (SOI) laterally diffused metal-oxide-semiconductor (LDMOS) device was increased by introducing a step P-type doping buried layer (SPBL) below the buried oxide (BOX). Device simulation software MEDICI 0.13.2 was used to investigate the electrical characteristics of the new devices. When the device was turned off, the SPBL could enhance the reduced surface field (RESURF) effect and modulate the lateral electric field in the drift region to ensure that the surface electric field was evenly distributed, thus increasing the lateral breakdown voltage (BV_lat). The enhancement of the RESURF effect while maintaining a high doping concentration in the drift region (N_d) in the SPBL SOI LDMOS resulted in a reduction in the substrate doping concentration (P_sub) and an expansion of the substrate depletion layer. Therefore, the SPBL both improved the vertical breakdown voltage (BV_ver) and suppressed an increase in the specific on-resistance (R_on,sp). The results of simulations showed a 14.46% higher TrBV and a 46.25% lower R_on,sp for the SPBL SOI LDMOS compared to those of the SOI LDMOS. As the SPBL optimized the vertical electric field at the drain, the turn-off non-breakdown time (T_nonbv) of the SPBL SOI LDMOS was 65.64% longer than that of the SOI LDMOS. The SPBL SOI LDMOS also demonstrated that TrBV was 10% higher, R_on,sp was 37.74% lower, and T_nonbv was 10% longer than those of the double RESURF SOI LDMOS.

The generation of test vectors is a key technique that affects the efficiency and fault detection rate of the boundary scan test. Aiming at the local optimal solution problem of the current common test vectors generation algorithm, this paper proposes a test vectors generation algorithm based on improved GA-AO* model, through which the test vectors are generated by using the idea of heuristic search and backtracking correction. In order to speed up the heuristic search, this paper designed a heuristic function with both prior and posterior parameters to describe the influence of typical faults on the failure probability index of the test vectors. At the same time, this paper used a genetic algorithm (GA) to determine the specific values of the posterior parameters iteratively. Finally, through theoretical analysis and physical verification, compared with the test vector generated by the traditional method, the test vector generated by this method is optimized on the prior failure probability index and performs better in the physical experiment.