The reliability of electronic systems, used in nuclear power plants, is traditionally estimated with empirical databases such as MIL HDBK-217, PRISM etc. These methods assign a constant failure rate to electronic devices, during their useful life. Currently, electronic reliability prediction is moving towards applying the Physics of Failure approach which considers information on process, technology, fabrication techniques, materials used, etc. The constant failure rate assumption stems from treating failures as random events. Electronics division of BARC is engaged in design & fabrication of CMOS and BJT ICs for nuclear pulse processing and signal conditioning. New microelectronic devices often exhibit infant mortality and wear-out phenomena while in operation. It points to competing degradation mechanisms-electro migration, hot carrier injection, dielectric breakdown etc.-that make a device's useful life different from that predicted by empirical methods.Understanding the dominant mechanisms that lead to device failure -Physics of Failure--is a more realistic approach to reliability prediction. This paper describes common failure mechanisms-encountered in CMOS and BJT ICs and the efforts being taken to quantify these effects in an optical-isolator IC -4N36-which forms a part of the trip generation circuit in neutron flux monitoring systems.
Path planning of a robot [1] is a problem in which a problem space or a problem domain with a number of obstacles is given and the aim is to find a collision free path which a robot can follow in order to reach its destination from the start position. Here in this paper environment is represented by ordered grids, each of which represents a location in the mobile robot movement space. Along with this we would be able to place dynamic no. of static and movable obstacles at run time. The boundary of obstacles is formed by their actual boundary plus minimum safety distance considering the size of the mobile robot, which makes it possible to treat the mobile robot a point in the environment. The path [2] which the robot will follow is desired to be optimal in terms of distance and time taken by the robot to reach the destination. Here in this problem we have used Genetic Algorithm for path planning which is a search algorithm based on the mechanics of natural selection and natural genetics. Potential solutions of a problem are chromosomes, which form a population of possible solutions which compete with each other on the basis of fitness function. A selection mechanism based on the fitness is applied to the population and the individuals strive for the survival.
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