Analog Circuits and Systems Optimization based on Evolutionary Computation Techniques [electronic resource] /
Barros, Manuel.author.
Guilherme, Jorge.author.
Horta, Nuno.author.
The microelectronics market trends present an ever-increasing level of complexity with special emphasis on the production of complex mixed-signal systems-on-chip. Strict economic and design pressures have driven the development of new methods to automate the analog design process. However, and despite some significant research efforts, the essential act of design at the transistor level is still performed by the trial and error interaction between the designer and the simulator. This book presents a new design automation methodology based on a modified genetic algorithm kernel, in order to improve efficiency on the analog IC design cycle. The proposed approach combines a robust optimization with corner analysis, machine learning techniques and distributed processing capability able to deal with multi-objective and constrained optimization problems. The resulting optimization tool and the improvement in design productivity is demonstrated for the design of CMOS operational amplifiers.
Bibliographic Level Mode of Issuance: Monograph
Includes bibliographical references and index.
State-of-the-Art on Analog Design Automation -- Evolutionary Analog IC Design Optimization -- Enhanced Techniques for Analog Circuits Design Using SVM Models -- Analog IC Design Environment Architecture -- Optimization of Analog Circuits and Systems - Applications -- Conclusions.
The microelectronics market trends present an ever-increasing level of complexity with special emphasis on the production of complex mixed-signal systems-on-chip. Strict economic and design pressures have driven the development of new methods to automate the analog design process. However, and despite some significant research efforts, the essential act of design at the transistor level is still performed by the trial and error interaction between the designer and the simulator. This book presents a new design automation methodology based on a modified genetic algorithm kernel, in order to improve efficiency on the analog IC design cycle. The proposed approach combines a robust optimization with corner analysis, machine learning techniques and distributed processing capability able to deal with multi-objective and constrained optimization problems. The resulting optimization tool and the improvement in design productivity is demonstrated for the design of CMOS operational amplifiers.
Electronics.
Microelectronics.
Computational intelligence.
Robotics.
Automation.
Electronics and Microelectronics, Instrumentation.
Computational Intelligence.
Robotics and Automation.
Studies in Computational Intelligence