The selectivity is one of the main challenges to develop a gas sensor, the good chemical species detection in a gaseous mixture decreasing the missed detections. The present paper proposes a new solution for gas sensor selectivity based on artificial neural networks (ANNs) and fuzzy logic (FL) algorithm. We first use ANNs to develop a gas sensor model in order to accurately express its behavior. In a second step, the FL and Matlab environment are used to create a database for a selective model, where the response of this one only depends on one chemical species. Analytical models for the gas sensor and its selective model are implemented into a Performance Simulation Program with Integrated Circuit Emphasis (PSPICE) simulator as an electrical circuit in order to prove the similarity of the analytical model output with that of the MQ-9 gas sensor where the output of the selective model only depends on one gas. Our results indicate the capability of the ANN-FL hybrid modeling for an accurate sensing analysis.

This paper proposes a new method based on a genetic algorithm (GA) approach to optimize the electrical parameters such as height barrier, ideality factor, fill factor, open-circuit voltage and power conversion efficiency, in order to improve the electrical performance of Schottky solar cells in an over wide range of temperature. Thus the parameters research process called objective function is used to find the optimal electrical parameters providing greater conversion efficiency. The proposed model results are also compared to experimental and analytical I-V data, where a good agreement has been found between them. Therefore, this approach may provide a theoretical basis and physical insights for Schottky solar cells.

The performances of two full differential operational amplifiers (Op-Amps) telescopic and folded-cascode are evaluated to satisfy the stringent requirements on the amplifier to be used in a Multiplying Digital-to-Analog Converter (MDAC) stage of a pipelined ADC (Analog-to-Digital Converter). The paper shows the solutions found to reach high gain, wide bandwidth and short settling time without degrading too much the output swing. The Op-Amp specifications are extracted according to the ADC requirements, then the two Op-Amp topologies are designed, tested and their performances are compared. Simulation results show that the Op-Amp folded-cascode topology is more suitable architecture for pipelined ADC than the telescopic one. Moreover, the use of this type of Op-Amp generates an Integral Non-Linearity (INL) error less than that of the telescopic one. The analyses and simulation results are obtained using 0.18 µm AMS (Austria Mikro System) CMOS process parameters with a power supply voltage of 1.8V. The predicted performance is verified by analysis and simulations using Cadence EDA simulator.

An 8-bit 40-MS/s low power Multiplying Digital-toAnalog Converter (MDAC) for a pipelined-to-Analog to Digital converter (ADC) is presented.The conventional dedicated operational amplifier (Op-Amp) isperformed 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 multiobjective genetic algorithms to allow automated optimization. The proposed program is used tofind 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 goodagreement is observed between the results obtained bythe program optimization and simulation, after that the Op-Ampis introduced in the MDAC circuit to extract its performances.