فهرست مطالب صادق محمدی اسفهرود

The performance of an Analog/Digital (A/D) converter, various aspects like general architecture of the converter, architecture of the building blocks or design of the blocks can be improved. The comparator block is a fundamental block in data converters. Due to contradicting design purposes, circuit constraints and necessities, design of comparators and obtaining best circuit performance are complicated and challenging. Such challenges in circuit design necessitate presenting approaches which not only satisfy all the objectives but also, they are cost effective in terms of time and cost. One of the approaches which has recently attracted attentions is the heuristic algorithms based intelligent Methods. Inclined Planes system Optimization algorithm (IPO) is a novel heuristic algorithm inspired by dynamic movement of the objects on frictionless inclined planes. But despite its remarkable ability for exploration and exploitation of the search space, its standard model has complex relationships with many structural parameters that often confuse the user in choosing the effective values for them.In this paper, IPO algorithm is simplified to present a heuristic algorithm (called SIPO) and its efficiency in optimization of 10 standard benchmarks has been evaluated. Then, a multi-objective version of the proposed algorithm (called MOSIPO) for design and optimization of double tail comparator is presented and its efficiency in optimization of double tail comparator has been evaluated and compared with popular multi-objective intelligent methods. The results clearly demonstrate the improved performance and superiority of SIPO and MOSIPO compared to the other methods.

The effectiveness of multi-objective optimization methods, especially the methods based on Swarm Intelligence, has led the researchers to utilize them significantly to solve complex engineering problems with multiple conflicting objectives. This paper aimed at evaluating the performance of new and powerful multi-objective heuristic methods based on Swarm Intelligence (including multi-objective versions of MOPSO, MOGWO, NSGSA, MOGOA, MOIPO, MOMIPO, and MOALO algorithms), and used them for optimal design of the Sense Amplifier-based Flip-Flop (SAFF) using 0.18-µm CMOS technology. In this paper, the channel's width values of the transistors as designing variables, and total average power and delay as the fitness values of the two objective functions were assessed and optimized in terms of multi-objective optimization problem using intelligent optimization algorithm based on Swarm Intelligence assumption in order to achieve the desired values of power-delay product (PDP). Comparing the results obtained for all of the above multi-objective optimization methods, the Multi-Objective Grasshopper Optimization Algorithm (MOGOA) performed better. This method was able to perform very well in the statistical indices of fitness and multi-objective optimization criteria in comparison with other methods. It creates an appropriate trade-off between conflicting objective functions with average power of 24 µW, delay of 95.4 ps and PDP of 2.29 fJ.