Solar Cell Parameter Extraction Method from Illumination and Dark I-V Characteristics.

A novel method to extract the seven parameters of the double-diode model of solar cells using the current-voltage (I-V) characteristics under illumination and in the dark is presented. The algorithm consists of two subroutines which are alternatively run to adjust all the parameters of the cell in an iterative process. Curve fitting of the light I-V characteristics ensures accuracy in the prediction of the maximum power point, whereas simultaneously fitting the dark I-V characteristics results in a set of physically meaningful parameters that provide information about the physical performance of the photovoltaic devices. Experimental I-V curves of in-house solar cells are used to validate the proposed parameter extraction method, which can be furthermore applied to other types of p-n junction-based photovoltaic devices.

High-Level Modeling and Simulation Tool for Sensor Conditioning Circuit Based on Artificial Neural Networks.

For current microelectronic integrated systems, the design methodology involves different steps that end up in the full system simulation by means of electrical and physical models prior to its manufacture. However, the higher the circuit complexity, the more time is required to complete these simulations, jeopardizing the convergence of the numerical methods and, hence, meaning that the reliability of the results are not guaranteed. This paper shows the use of a high-level tool based on Matlab to simulate the operation of an artificial neural network implemented in a mixed analog-digital CMOS process, intended for sensor calibration purposes. The proposed standard tool enables modification of the neural model architecture to adapt its characteristics to those of the electronic system, resulting in accurate behavioral models that predict the complete microelectronic IC system behavior under different operation conditions before its physical implementation with a simple, time-efficient, and reliable solution.

Low-Power Highly Robust Resistance-to-Period Converter.

This paper presents a novel structure of Resistance- to-Period (R-T) Converter highly robust to supply and temperature variations. Robustness is achieved by using the ratiometric approach so that complex circuits or high accuracy voltage references are not necessary. To prove the proposed architecture of R-T converter, a prototype was implemented in a 0.18 µ m CMOS process with a single supply voltage of 1.8 V and without any stable reference voltage. Experimental results show a maximum ±1.5% output signal variation for ±10% supply voltage variation and in a 3⁻95 ° C temperature range.

An integrated low-power lock-in amplifier and its application to gas detection.

This paper presents a new micropower analog lock-in amplifier (LIA) suitable for battery-operated applications thanks to its reduced size and power consumption as well as its operation with single-supply voltage. The proposed LIA was designed in a 0.18 µm CMOS process with a single supply voltage of 1.8 V. Experimental results show a variable DC gain ranging from 24.7 to 42 dB, power consumption of 417 µW and integration area of 0.013 mm2. The LIA performance was demonstrated by measuring carbon monoxide concentrations as low as 1 ppm in dry N2. The experimental results show that the response to CO of the sensing system can be considerably improved by means of the proposed LIA.