Reliability-Aware Support Vector Machine-Based High-Level Surrogate Model for Analog Circuits PROJECT TITLE :Reliability-Aware Support Vector Machine-Based High-Level Surrogate Model for Analog CircuitsABSTRACT:Negative bias temperature instability (NBTI) has deleterious impact on threshold voltage and drive current of PMOS transistor. During this paper, a support vector machine (SVM)-based surrogate model (SM) for NBTI phenomenon is developed at intervals the framework of the HSPICE MOSFET reliability analysis (MOSRA) model for gain and slew rate of a differential amplifier. Feasibility area identification and adaptive learning scheme are applied to boost the results using less number of coaching samples, that reduces run time. One performance parameter analysis of complete circuit requires zero.046 ms and 0.03 ms for gain and slew rate, respectively, that shows important improvement over previous methodology. This SM is validated by finding a correlation coefficient between the SVM model and HSPICE. The values of the correlation coefficients are zero.9979 and zero.9997 for gain and slew rate, respectively. Did you like this research project? To get this research project Guidelines, Training and Code... Click Here facebook twitter google+ linkedin stumble pinterest Telerobotic Haptic Exploration in Art Galleries and Museums for Individuals with Visual Impairments Guest Editorial Multimodal Modeling and Analysis Informed by Brain Imaging—Part I
