AI techniques for reliability prediction for electronic components

In the industry of manufacturing and design, one major constraint has been enhancing operating performance using less time. As technology continues to advance, manufacturers are looking for better methods in predicting the condition and residual lifetime of electronic devices in order to save repair costs and their reputation. Intelligent systems are a solution for predicting the reliability of these components; however, there is a lack of research on the advancements of this smart technology within the manufacturing industry. AI Techniques for Reliability Prediction for Electronic Components.

Description based upon print version of record.

Chapter 1. Reliability analysis: need and techniques -- Chapter 2. Reliability study of polymers -- Chapter 3. Reliability of CNTFET and NW-FET devices -- Chapter 4. Traditional and non-traditional optimization techniques to enhance reliability in process industries -- Chapter 5. Residual life estimation of humidity sensor DHT11 using artificial neural networks -- Chapter 6. Nanocomposite-based humidity sensor: reliability prediction using artificial intelligence techniques -- Chapter 7. Role of artificial neural network for prediction of gait parameters and patterns -- Chapter 8. Modelling analysis and simulation for reliability prediction for thermal power system -- Chapter 9. High level transformation techniques for designing reliable and secure DSP architectures -- Chapter 10. Design for testability of high-speed advance multipliers: design for testability -- Chapter 11. A novel moth-flame algorithm for PID-controlled processes with time delay -- Chapter 12. Artificial intelligence for interface management in wireless heterogeneous networks -- Chapter 13. PVT variability check on UCM architectures at extreme temperature-process changes -- Chapter 14. Frequency-based RO-PUF -- …