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Project Title/Subject: Fault Detection and Diagnosis in VLSI Circuits using Machine Learning
Description: Fault detection and diagnosis is an important aspect of VLSI design and testing. This project aims to develop a machine learning-based approach for detecting and diagnosing faults in VLSI circuits. The project will use a dataset containing various VLSI circuit characteristics such as power, delay, and area, along with fault information. The data will be used to train a machine learning model to identify faults in VLSI circuits. The model will be optimized for accuracy and reliability to ensure that it can detect even the smallest faults in the circuit. The project will involve selecting appropriate machine learning algorithms, pre-processing and cleaning the data, selecting relevant features, and optimizing the model to achieve the highest possible accuracy.
What you can expect: Develop an accurate and reliable machine learning model for detecting and diagnosing faults in VLSI circuits Provide insights into the most important factors contributing to VLSI circuit faults Improve the efficiency and effectiveness of VLSI testing and design Tools and Technologies: Python, Scikit-learn, TensorFlow, Pandas, NumPy, Matplotlib, Jupyter Notebook, Cadence Virtuoso, and HSPICE.
You have to do some academic background study , so here are some IEEE papers related to the project idea:
"Fault Diagnosis in VLSI Circuits Using Machine Learning Techniques" by H. D. Pratama, S. N. K. R. Iyengar, and R. Ganesan. IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems, vol. 37, no. 9, pp. 1847-1858, Sep. 2018. (Link)
"A Machine Learning Approach to Fault Diagnosis in VLSI Circuits" by S. Saha, S. P. Mohanty, and M. B. Srinivas. IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems, vol. 35, no. 11, pp. 1821-1831, Nov. 2016. (Link )
"Design for Testability Techniques for VLSI Circuits: A Comprehensive Survey" by S. M. M. Islam, H. K. Singh, and M. U. Mahmud. IEEE Transactions on Very Large Scale Integration (VLSI) Systems, vol. 26, no. 6, pp. 1115-1129, June 2018. (Link)
"Fault-Tolerant VLSI Design Using Machine Learning Techniques" by M. M. Rahman and S. P. Mohanty. IEEE Transactions on Very Large Scale Integration (VLSI) Systems, vol. 28, no. 9, pp. 2048-2056, Sep. 2020. (Link)
Here are some ScienceDirect thesis links related to the project idea:
"Fault Detection and Diagnosis in VLSI Circuits Using Machine Learning" by R. Satyavathi, Sri Venkateswara University, 2019. (Link)
"Fault Diagnosis in Analog Circuits using Machine Learning Techniques" by J. K. B. Ganesh, Indian Institute of Technology Madras, 2019. (Link)
"Design and Analysis of Machine Learning-based Diagnosis Techniques for VLSI Circuits" by G. Sankaranarayanan, National Institute of Technology Tiruchirappalli, 2021. (Link)
"Fault Detection and Diagnosis in VLSI Circuits using Machine Learning Techniques" by K. M. Kishore, Sri Venkateswara University, 2019. (Link) Here are some thesis links from Google Scholar related to the project idea:
"Fault Diagnosis in VLSI Circuits using Machine Learning Techniques" by S. Saha, S. P. Mohanty, and M. B. Srinivas. International Journal of Electronics, vol. 103, no. 7, pp. 1078-1096, July 2016. (Link)
"Fault Detection and Diagnosis in VLSI Circuits using Machine Learning" by N. N. V. Kumar, K. Prasanth, and S. Aravind. International Journal of Innovative Research in Science, Engineering and Technology, vol. 8, no. 1, pp. 53-62, Jan. 2019. (Link)
"A Machine Learning-based Approach for Fault Detection and Diagnosis in VLSI Circuits" by P. C. Patra and S. R. Panigrahy. International Journal of Emerging Trends & Technology in Computer Science, vol. 7, no. 2, pp. 8-15, Mar.-Apr. 2018. (Link)
"Fault Diagnosis in VLSI Circuits using Machine Learning Techniques" by S. V. Thirumalai and R. S. Rajesh. International Journal of Electrical and Computer Engineering, vol. 6, no. 2, pp. 759-768, Apr. 2016. (Link)
These papers/thesis can provide additional insights and information to help you further develop and enhance your project.
This project can be expanded to include other aspects of VLSI design and testing such as fault-tolerant design, yield optimization, and design for testability. Additionally, the project can be extended to other fields of engineering where fault detection and diagnosis is critical, such as automotive, aerospace, and telecommunications.
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