Study of Microscale Meniscus Confined Electrodeposition Based on COMSOL

The rate and quality of microscale meniscus confined electrodeposition represent the key to micromanipulation based on electrochemistry and are extremely susceptible to the ambient relative humidity, electrolyte concentration, and applied voltage. To solve this problem, based on a neural network and genetic algorithm approach, this paper optimizes the process parameters of the microscale meniscus confined electrodeposition to achieve high-efficiency and -quality deposition. First, with the COMSOL Multiphysics, the influence factors of electrodeposition were analyzed and the range of high efficiency and quality electrodeposition parameters were discovered. Second, based on the back propagation (BP) neural network, the relationships between influence factors and the rate of microscale meniscus confined electrodeposition were established. Then, in order to achieve effective electrodeposition, the determined electrodeposition rate of 5*10−8 m/s was set as the target value, and the genetic algorithm was used to optimize each parameter. Finally, based on the optimization parameters obtained, we proceeded with simulations and experiments. The results indicate that the deposition rate maximum error is only 2.0% in experiments. The feasibility and accuracy of the method proposed in this paper were verified.

Design Of Fuzzy Rule-Based System Using Genetic Algorithms

The aim of this paper is to obtain a compact and optimal fuzzy rule-based model from observation data by utilizing the Genetic algorithm technique. The approach is optimized by applying Genetic Algorithms, owing to its capability of searching irregular and high dimensional solution spaces. Genetic Algorithms has been applied to learn consequent part of fuzzy rules and models with fixed number of rules. In the work we propose a Genetic algorithm approach to a non-linear air conditioning system for the construction of optimal fuzzy rules in two steps. First, fuzzy clustering is applied to obtain an initial rule based model having pre-calculated number of rules with antecedents only. In the second step, the regions of rule-consequents are obtained by a binary coded Genetic Algorithm which leads to the extraction of an optimal rule based model.

Genetic Algorithm-Based Digital Test Optimization Method and its Application to Yield Improvement

Narrowing design and manufacturing process margins with technology scaling are one of the causes for a reduction in IC chip test margin. This situation is further aggravated by the extensive use of third-party design blocks in contemporary system-on-chips which complicates chip timing constraint. Since a thorough timing verification prior to silicon fabrication is usually not done due to aggressive product launch schedules and escalating design cost, occasionally, a post-silicon timing optimization process is required to eliminate false fails encountered on ATE. An iterative two-dimensional shmoo plots and pin margin analysis are custom optimization methods to accomplish this. However, these methods neglect the interdependencies between different IO timing edges such that a truly optimized condition cannot be attained. In this paper, we present a robust and automated solution based on a genetic algorithm approach. Elimination of shmoo holes and widening of test margins (up to 2x enhancements) are demonstrated on actual product test cases. Besides test margin optimization, this method also offers insights into the criticality of test pins to accelerate failure debug turnaround time.