Applications of evolutionary computation techniques to analog, mixed-signal and RF circuit design - an overview <s> I. INTRODUCTION <s> We present a methodology for automated sizing of analog cells using statistical optimization in a simulation based approach. This methodology enables us to design complex analog cells from scratch within reasonable CPU time. Three different specification types are covered: strong constraints on the electrical performance of the cells, weak constraints on this performance, and design objectives. A mathematical cost function is proposed and a bunch of heuristics is given to increase accuracy and reduce CPU time to minimize the cost function. A technique is also presented to yield designs with reduced variability in the performance parameters, under random variations of the transistor technological parameters. Several CMOS analog cells with complexity levels up to 48 transistors are designed for illustration. Measurements from fabricated prototypes demonstrate the suitability of the proposed methodology. <s> BIB001 </s> Applications of evolutionary computation techniques to analog, mixed-signal and RF circuit design - an overview <s> I. INTRODUCTION <s> The paper gives an overview of methods and tools that are needed to design and embed analogue and RF blocks in mixed-signal integrated systems on chip (SoC). The design of these SoCs is characterised by growing design complexities and shortening time to market constraints. This requires new mixed-signal design methodologies and flows, including high-level architectural explorations and techniques for analogue behavioural modelling. This also calls for new methods to increase analogue design productivity, such as the reuse of analogue blocks as well as the adoption of analogue and RF circuit and layout synthesis tools. Also, more detailed modelling and verification tools are needed that can analyse signal integrity and crosstalk problems, especially noise coupling problems caused by the embedding of the analogue circuits in a digital environment. Solutions that already exist today are presented, and challenges that still remain to be solved are outlined. <s> BIB002 </s> Applications of evolutionary computation techniques to analog, mixed-signal and RF circuit design - an overview <s> I. INTRODUCTION <s> An optimization tool for radio frequency integrated circuits (RFICs) based on an elitist nondominated sorting genetic algorithm is introduced. It casts RF circuit synthesis as a multi-objective optimization problem and produces multiple solutions along the Pareto optimal front. Optimization is followed by sensitivity assessment wherein Monte Carlo simulations are performed for the Pareto points with respect to process, voltage, and temperature variations. The tool is validated in the synthesis of a 5.2-GHz direct-conversion receiver front-end that includes a common-gate differential low-noise amplifier, I/Q down-conversion mixers, and a quadrature voltage-controlled oscillator in a 250-nm SiGe BiCMOS process. <s> BIB003
Strategies to automate sizing of analog, mixed-signal and radio-frequency (AMS/RF) circuits have usually followed either a knowledge-based approach or an optimization-based approach. These strategies aim at finding the optimal sizing of the problem's variables that satisfies imposed constraints and maximize/minimize some objective functions, if any. Knowledge-based approaches suffer from well-known limitations. Therefore, optimization-based approaches have become dominant for the past 15 years BIB002 . It is, however, important to note that the addition of AMS/RF design knowledge significantly enhances the optimization results by improving the design space exploration. Optimization-based approaches formulate the design problem as a constrained optimization problem: minimize/maximize some objective functions (e.g., minimize power consumption, minimize area), subject to some constraints (e.g., slew rate larger than a certain value). This can be formulated as follows: [ ] Optimization algorithms can be classified into two major classes: exploration (global) and exploitation (local) algorithms. Exploitation approaches, which can be deterministic or not, are appropriate for fine-tuning of designs, but not for sizing starting from scratch. On the other hand, exploration algorithms are used in global sizing. Most famous ones are heuristics, and, probably, the most widely used heuristics in AMS/RF design have been genetic algorithms, simulated annealing and modified versions of them (e.g. BIB003 , BIB001 ). In this paper, we will review some evolutionary computation (EC) techniques and their application to AMS/RF circuit design problems. Basic techniques are referenced in Section II and special attention is given to multi-objective problems in Section III. AMS/RF design problems are usually heavily constrained problems; hence, constraint handling mechanisms are addressed in Section IV and some current challenges are discussed in Section V.