1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 60 61 62 63 64 65 Two Stage CMOS Operational Transconductance Amplifier for Front-End Electronics Design using Multiobjective Genetic Algorithms Abdelghani Dendouga, Slimane Oussalah Division Microélectronique et Nanotechnologie Centre de Développement des Technologies Avancées Algiers, Algeria adendouga@cdta.dz Abstract—In this paper, we elaborate a program based on multi-objective genetic algorithms (MOGAs) to allow automated optimization of analog circuits. The proposed methodology is used to find the optimal transistors sizes (length and width) in order to obtain operational amplifier performances for analog and mixed CMOS-based circuit applications. Eight performances are considered in this study, direct current (DC) gain, unity-gain bandwidth (GBW), phase margin (PM), power consumption (P), area (A), slew rate (SR), thermal noise and signal to noise ratio (SNR). The program is solved using MATLAB Optimization Toolbox™ solvers. Also by using variables obtained from genetic algorithms, the operational transconductance amplifier (OTA) is simulated by using Cadence Virtuoso Spectre circuit simulator in standard TSMC (Taiwan Semiconductor Manufacturing Company) RF 0.18μm CMOS technology. A good agreement is observed between the program optimization and electric simulation. Keywords—CMOS analog circuit design; multi-objective genetic algorithms; optimization; two stage OTA I. INTRODUCTION System-level design automation of analog circuits remains an important challenge for the semiconductor industry. Traditional manual top-down constrained design methodologies for analog circuits require significant designer expertise. An unknown number of design iterations may arise due to lack of knowledge of potential sub-block performance limitations. This process will achieve a finalized design that may not be optimal in terms of performance or power consumption. Advances in design automation techniques provide methodologies to ensure the design of optimal analog circuits and systems [1]. To fulfill the given requirements, the designer must choose the suitable circuit architecture. Many multi-objective optimization methods have been developed over the past years [2-4]. These methods can generally be classified under the two main categories; weighted or aggregated approaches and the Pareto-based approaches. Analog circuit design is a hard and tedious work due to the large number of parameters to be optimized, constraints, and performances that the designer has to handle. In spite of its importance, analog design automation still lags behind that of digital circuits [5]. Therefore, the use of multiple-objective optimization algorithms is of a great importance to the automatic design of analog circuit. Accuracy, ease of use, generality, robustness, and reasonable run-time are necessary for a circuit synthesis solution to gain acceptance by using optimization methods [6]. This method uses a program based on multi-objective optimization using a genetic algorithm to calculate the optimal transistors dimensions, length and width, of a two-stage CMOS operational transconductance amplifier (Fig. 1) which is used as part of an electronic front-end for signal shaping stage. The method which handles a wide variety of specifications and constraints, is extremely fast, and results in globally optimal designs. The target of this study is to design and optimize a two-stage operational amplifier circuit in sight of a front-end electronics of the semiconductor tracker (SCT) which forms a vital part of the ATLAS (A Toroidal LHC Apparatus) experiment. ATLAS is a particle physics experiment at the Large Hadron Collider (LHC) at CERN (the European Organization for Nuclear Research) in Switzerland [7]. This paper is organized as follows. Section 2 gives the basic introduction of genetic algorithms and optimization procedure. The operational transconductance amplifier structure is analyzed in section 3. Section 4 describes the optimization approach proposed in this work. The obtained results are presented in section 5. Finally some concluding remarks are provided in the last section. II. DESIGN METHODOLOGY Optimal design of analog circuits consists of finding a variable set x ={x 1 , x 2 ,…, x n } that optimizes a performance functions, such as gain, offset, signal to noise ratio, maximum operating frequency etc., while meeting imposed specifications and/or inherent constraints, for example, saturation conditions of transistors, technology limits, impedance matching, etc. Vector x may encompass biases, lengths (L) and widths (W) of MOSFET gate transistors, component values, etc [5]. 1 2015 12th International Multi-Conference on Systems, Signals & Devices 978-1-4799-1758-7/15/$31.00 ©2015 IEEE