Reducing Library Characterization Time for Cell-aware Test while Maintaining Test Quality

Cell-aware test (CAT) explicitly targets faults caused by defects inside library cells to improve test quality, compared with conventional automatic test pattern generation (ATPG) approaches, which target faults only at the boundaries of library cells. The CAT methodology consists of two stages. Stage 1, based on dedicated analog simulation, library characterization per cell identifies which cell-level test pattern detects which cell-internal defect; this detection information is encoded in a defect detection matrix (DDM). In Stage 2, with the DDMs as inputs, cell-aware ATPG generates chip-level test patterns per circuit design that is build up of interconnected instances of library cells. This paper focuses on Stage 1, library characterization, as both test quality and cost are determined by the set of cell-internal defects identified and simulated in the CAT tool flow. With the aim to achieve the best test quality, we first propose an approach to identify a comprehensive set, referred to as full set, of potential open- and short-defect locations based on cell layout. However, the full set of defects can be large even for a single cell, making the time cost of the defect simulation in Stage 1 unaffordable. Subsequently, to reduce the simulation time, we collapse the full set to a compact set of defects which serves as input of the defect simulation. The full set is stored for the diagnosis and failure analysis. With inspecting the simulation results, we propose a method to verify the test quality based on the compact set of defects and, if necessary, to compensate the test quality to the same level as that based on the full set of defects. For 351 combinational library cells in Cadence’s GPDK045 45nm library, we simulate only 5.4% defects from the full set to achieve the same test quality based on the full set of defects. In total, the simulation time, via linear extrapolation per cell, would be reduced by 96.4% compared with the time based on the full set of defects.

Statistics Based Neural Networks Method for Industrial Image Inspection

Automated industrial image inspection system has attracted a great deal of interest in recent years. In this thesis, a new method is presented by combining a statistics method with a neural networks method, which could reduce the interference of machine dynamics and improve the inspection accuracy. Different from the pixel-based or feature-based methods, the proposed method is based on two indices of an image, which are the variances of the rows and columns of the image. For image inspection, first neural networks are trained using these two indices from a set of good images in order to establish a tolerance zone. Then, the two indices of each inspection image are computed through trained neural networks and compared with the tolerance zone. A defective item is detected if either index falls out of the tolerance zone. The other contributions, such as two-point based image registration method and defect simulation algorithms, also help to improve the performance of inspection. Experimental results demonstrate that the proposed approach has a better performance in comparison with traditional statistics approach.

Statistics Based Neural Networks Method for Industrial Image Inspection

Automated industrial image inspection system has attracted a great deal of interest in recent years. In this thesis, a new method is presented by combining a statistics method with a neural networks method, which could reduce the interference of machine dynamics and improve the inspection accuracy. Different from the pixel-based or feature-based methods, the proposed method is based on two indices of an image, which are the variances of the rows and columns of the image. For image inspection, first neural networks are trained using these two indices from a set of good images in order to establish a tolerance zone. Then, the two indices of each inspection image are computed through trained neural networks and compared with the tolerance zone. A defective item is detected if either index falls out of the tolerance zone. The other contributions, such as two-point based image registration method and defect simulation algorithms, also help to improve the performance of inspection. Experimental results demonstrate that the proposed approach has a better performance in comparison with traditional statistics approach.

Research on Defect Diagnosis Method of Reactor Acoustic Vibration Method Based on Deep Learning

Although the state evaluation method based on characteristic parameters and weight factors can extract the characteristic quantities in time domain and frequency domain according to the collected acoustic and vibration signals of reactors, it is necessary to analyze a large number of test data to establish the functional relationship between the characteristic quantities and the defect states, and to establish the function relationship between the characteristic quantities and the defect states, and to establish the function relationship between the characteristic quantities and the defect states The method can directly learn the data samples, and self-study the correlation rules of characteristic parameters and defects through the training of neural network. In this paper, the deep learning neural network model is constructed, and the data obtained from reactor defect simulation experiment and field measurement are used as samples to train the deep learning network. Through the training of neural network, the characteristics of acoustic vibration signal are automatically learned, and the characteristics are stored in the parameters of neural network. Finally, the state of reactor is realized by the classifier at the end of the network assessment

Highest Ionic Conductivity of BIMEVOX (ME = 10% Cu, 10% Ga, 20% Ta): Computational Modeling and Simulation

BIMEVOX had the potential to play an important role in solid oxide fuel cell, especially as the electrolyte due to their high ionic conductivity. In this work, oxide ion migrations of γ-Bi2VO5.5 and BIMEVOX were simulated using density function theory (DFT), Mott-Littleton method, and molecular dynamic simulation. In γ-Bi2VO5.5, there were oxygen vacancies at the equatorial position in the vanadate layers. These vacancies could facilitate oxide ions migration. The Enthalpy of the oxide migration for γ-Bi2VO5.5 based on DFT calculation was 0.38 eV, which was in a good agreement with experimental results. The γ-Bi2VO5.5 can be stabilized by partial substitution of V5+ with Cu2+, Ga3+, and Ta5+. Defect simulation results using the Mott-Littleton method showed that the total maximum energies of region II were achieved at concentrations of 10, 10, and 20%, respectively for Cu2+, Ga3+, and Ta5+. The calculated concentration of Cu2+, Ga3+, and Ta5+ were in a good agreement with those of experiment results, where the highest ionic conductivity obtained. The results of the molecular dynamics simulation showed that the activation energies of oxide ion migration in γ-Bi2VO5.5 and BIMEVOX (ME = Cu and Ta) respectively were 0.19, 0.21, and 0.10 eV, close to experimental values.