http://localhost:8080/xmlui/handle/123456789/7167 2026-04-12T03:26:30Z http://localhost:8080/xmlui/handle/123456789/7207 Reduction of test timeduring design for testability Patel, Maharshi As VLSI technology is continuously shrinking to lower technology nodes we need efficient technique for testing. Now, reliability and testability both are the important parameters in today’s VLSI design. Reducing the testing time is major challenge in scan based DFT (or test) the sequence that, when applied to a digital circuit, it will enables automatic test equipment to distinguish between the correct circuit behavior and the faulty circuit behavior caused by defects. Now, ATE machines are very expensive machine i.e. (i) more number of test patterns will take more time to execute and that result in more cost. (ii) more data architecture for cost-effective test. So, more pattern volume will require more storage capacity. Larger pattern volume need more time for scan operation in DUT also. DFT Compiler from Synopsys is used to generate the verified scan design. ATPG tool generate vectors that can detect volume needed more memory to store, that will result in more cost. The ATPG tool generates a statistics report later that tells us what the tool has done and provides fault category information that we have to interpret to debug coverage problems. Test-time improvement by reordering the scan cells as per priority is the main focus of this dissertation. I achieved significant DFT-debugging time of 19.56% with compare to normal scan operation by adding STCPI and reordering the scan chains. For Full Thesis Kindly contact to respective Library 2019-05-01T00:00:00Z http://localhost:8080/xmlui/handle/123456789/7168 Face detection for student attendance using deep learning Shah, Milan This Face detection is the task of identifying an individual student’s face and their database recognized deep learning. Face detection is largest technology with attendance of students.The system uses viola Jones algorithm in facial features. The student’s features compare to faces between system managers them efficient. The features allow to compare faces between system manager them efficient. Used SVM(support vector machine).The student’s faces detection local binary pattern and Recognition and facing detection method codes used sequentially. This project statistical approach using data objects which included method analysis. For Full Thesis Kindly contact to respective Library 2019-04-01T00:00:00Z