An approach to computer-aided interpretation of parametric test data for integrated circuit process-problem diagnosis is presented. In contrast to a conventional expert system, which reasons with a knowledge base consisting of rules acquired from human experts, the system presented centers its knowledge around analytic device equations. By using equations as the basis of the system knowledge, a more universal, well-organized, and concise level of knowledge is encoded. With the use of objects to present this knowledge, a great deal of useful information outside that described by only the symbolic expressions can be represented, and extension to qualitative or numeric models should be straightforward. The result is an expressive, well-organized, easily built, and easily maintained knowledge base. The authors describe the system's interactive graphical displays and the automatic data interpretation algorithms. Examples evaluating n-channel metal oxide semiconductor (NMOS) parameter variations, interconnect parameter variations, and interconnect yields are used for illustration 相似文献
In this paper,we address the need for better hands-on learning materials in computing education and the need for integrating mobile computing education into computing curricular.We propose an innovative learning approach that uses state-of-the-art mobile computing technologies and devices to design a highly accessible and real-world relevant hands-on labware for computing education.The labware employs a modular design such that the learning materials can both be combined together into a single course of mobile program development and be integrated into the related computing courses as building blocks.The labware will help students understand the concepts,improve their problem solving skills,and prepare them for the mobile computing industrial workforce.The pilot modules of the labware have been presented to undergraduate students and have received positive feedbacks. 相似文献
Surrogate models have been widely applied to correlate design variables and performance parameters in turbomachinery optimization applications. With more design variables and uncertain factors taken into account in an optimization design problem, the mathematical relations between the design variables and the performance parameters might present linear, low-order nonlinear or even high-order nonlinear characteristics, and are usually analytically unknown. Therefore, it is required that surrogate models have high adaptability and prediction accuracy for both the linear and nonlinear characteristics. The paper mainly investigates the effectiveness of an adaptive region segmentation combining surrogate model based on support vector regression and kriging model applied to a transonic axial compressor to approximate the complicated relationships between geometrical variables and objective performance outputs with different sampling methods and sizes. The purpose is to explore the prediction accuracy and computational efficiency of this adaptive surrogate model in real turbomachinery applications. Three different sampling techniques are studied: (1) uniform design; (2) Latin hypercube sampling method; (3) Sobol quasi-random design. For the low dimensional case with five variables, the adaptive region segmentation combining surrogate model performs better (not worse) than the single component surrogate in terms of prediction accuracy and computational efficiency. In the meanwhile, it is also noted that the uniform design applied to the adaptive surrogate model has more advantages over the Latin hypercube sampling method especially for the small sample size cases, both performing better than the Sobol quasi-random design. Moreover, a high dimensional case with 12 variables is also utilized to further validate the prediction advantage of the adaptive region segmentation combining surrogate model over the single component surrogate, and the computational results favor it. Overall, the adaptive region segmentation combining surrogate model has produced acceptable to high prediction accuracy in presenting complex relationships between the geometrical variables and the objective performance outputs and performed robustly for a transonic axial compressor problem.