Multiobjective Genetic Algorithms Program for the Optimization of an OTA for Front-End Electronics

Devi²,

et al. 2023

Biomimetics

The application of artificial intelligence in everyday life is becoming all-pervasive and unavoidable. Within that vast field, a special place belongs to biomimetic/bio-inspired algorithms for multiparameter optimization, which find their use in a large number of areas. Novel methods and advances are being published at an accelerated pace. Because of that, in spite of the fact that there are a lot of surveys and reviews in the field, they quickly become dated. Thus, it is of importance to keep pace with the current developments. In this review, we first consider a possible classification of bio-inspired multiparameter optimization methods because papers dedicated to that area are relatively scarce and often contradictory. We proceed by describing in some detail some more prominent approaches, as well as those most recently published. Finally, we consider the use of biomimetic algorithms in two related wide fields, namely microelectronics (including circuit design optimization) and nanophotonics (including inverse design of structures such as photonic crystals, nanoplasmonic configurations and metamaterials). We attempted to keep this broad survey self-contained so it can be of use not only to scholars in the related fields, but also to all those interested in the latest developments in this attractive area.

Section: Applications In Microelectronicsmentioning

confidence: 99%

A Comprehensive Review of Bio-Inspired Optimization Algorithms Including Applications in Microelectronics and Nanophotonics

Devi²,

et al. 2023

Biomimetics

“…The social behavior of fish and birds is described by a mathematical model based on the basic properties used to describe complex systems of self-regulation [12], [34]. This model was developed by Kennedy and Eberhart in 1995 and called PSO.…”

Section: Particle Swarm Optimization Algorithmmentioning

confidence: 99%

“…In previous years, the bipolar operational transconductance amplifier (OTA) was launched commercially [12]. In both open and closed loop electrical circuits, CMOS OTA has emerged as a crucial component [13].…”

Section: Introductionmentioning

confidence: 99%

Performance parameters optimization of CMOS analog signal processing circuits based on smart algorithms

Rasheed

Motlak

2023

Bulletin EEI

Designing ideal analogue circuits has become difficult due to extremely large-scale integration. The complementary metal oxide semiconductor (CMOS) analog integrated circuits (IC) could use an evolutionary method to figure out the size of each device. The CMOS operational transconductance amplifier (CMOS OTA) and the CMOS current conveyor second generation (CMOS CCII) are designed using advanced nanometer transistor technology (180 nm). Both CMOS OTA and CMOS CCII have high performance, such as a wide frequency, voltage gain, slew rate, and phase margin, to include very wide applications in signal processing, such as active filters and oscillators. The optimization approach is an iterative procedure that uses an optimization algorithm to change design variables until the optimal solution is identified. In this study, different sorts of algorithms the genetic algorithm (GA), particle swarm optimization (PSO), and cuckoo search (CS) are employed to boost and enhance the performance parameters. While decreasing the time required to develop a conventional operation amplifier's settling time. Some studies decrease the value of the power utilized at various frequencies. Others operate at extremely high frequencies, but their power consumption is greater than that of those operating at lower frequencies.

“…Therefore, the use of multiple-objective optimization algorithms (MOGAs) is of a great importance to the automatic design of Op-Amp. Accuracy, ease of use, generality, robustness, and reasonable run-time are all necessary for a circuit synthesis solution to gain acceptance by using optimization methods [6][7][8]. This method uses a program based on multi-objective optimization using a genetic algorithm (GA) to calculate the optimal transistors dimensions (length and width) of transistors, of an Op-Amp which is used in the Multiplying Digital-to-Analog Converter (MDAC).…”

Section: Introductionmentioning

confidence: 99%

Telescopic Op-Amp Optimization for MDAC Circuit Design

Dendouga¹,

Oussalah²

2017

ELS

Self Cite

An 8-bit 40-MS/s low power Multiplying Digital-to-Analog Converter (MDAC) for a pipelined-to-Analog to Digital converter (ADC) is presented. The conventional dedicated operational amplifier (Op-Amp) is performed by using telescopic architecture that features low power and less-area. Further reduction of power and area is achieved by using multifunction 1.5bit/stage MDAC architecture. The design of the Op-Amp is performed by the elaboration of a program based on multi objective genetic algorithms to allow automated optimization. The proposed program is used to find the optimal transistors sizes (length and width) in order to obtain the best Op-Amp performances for the MDAC. In this study, six performances are considered, direct current gain, unity-gain bandwidth, phase margin, power consumption, area, slew rate, thermal noise, and signal to noise ratio. The Matlab optimization toolbox is used to implement the program. Simulations were performed by using Cadence Virtuoso Spectre circuit simulator in standard AMS 0.18μm CMOS technology. A good agreement is observed between the results obtained by the program optimization and simulation, after that the Op-Amp is introduced in the MDAC circuit to extract its performances.