R. Dwivedi

Sensing mechanism in tin oxide-based thick-film gas sensors

A model has been developed for the sensing mechanism of metal oxide-based thick-film gas sensors. The model explains the behaviour of the sensor conductance as a function of the concentration of test gas and the operating temperature of the sensor. Using the Schottky-barrier conduction mechanism through grain boundaries, a relationship between the degree of surface coverage θ and the conductance G has been obtained. To relate the conductance with the concentration of the gas, the Freundlich adsorption isotherm for gases and vapours on a solid surface has been used. The isotherm relates the degree of surface coverage θ with the partial pressure of the gas (concentration). By eliminating θ, an expression relating the variation of G with concentration has been obtained. To study the validity of the model, a thick-film Pd-doped tin oxide gas sensor has been fabricated and tested with propanol (C3H7OH). The variation in the conductance with changes in concentration and temperature has been observed. The observed data show an excellent fit with the developed model. Using the experimental data, the constants of the theoretical equation have also been evaluated.

Sensing properties of palladium-gate MOS (Pd-MOS) hydrogen sensor-based on plasma grown silicon dioxide

Abstract This paper deals with the performance of a palladium-gate MOS hydrogen sensor studied by conductance method. Structure of the device was fabricated on a n-type 〈100〉 silicon wafer having resistivity of 1–6 Ω cm using plasma technology. Sensitivity and response–recovery time of the fabricated sensor have been studied for different concentration (1480–11 840 ppm) of hydrogen with varying signal frequency (500 Hz, 10 and 100 kHz) at room temperature. Hydrogen-induced interface-trapped density ( N it ) has been also evaluated as a function of gas concentration using a bias scan conductance method. Obtained results show that device performance is improved (i.e., high sensitivity and low response recovery time) and further it has been concluded that implementation of plasma technology (i.e., dry plasma cleaning of Si surface and in-situ RF anodization of Silicon in oxygen plasma near room temperature) may be a future step towards development of MOS-based sensors and integrated arrays with improved performance at room temperature.

Development of high sensitivity tin oxide based sensors for gas/odour detection at room temperature

Abstract An effort has been made to develop thick film tin oxide gas sensors which could detect various gases/odours at room temperature. To achieve this, the fabricated sensors were annealed in oxygen plasma for various durations. It was then found that, the room temperature sensitivity of such sensors was increased to about ten times as compared to the sensitivity of the non-annealed sensors. Further, plasma annealed sensors are found to be practically independent of temperature and the room temperature sensitivity of these sensors are found to be about 1.5 times the sensitivity of the conventional sensors at its operating temperature of 300°C. Studies on the variation of d.c. resistance, sensitivity, temporal response, current–temperature characteristics and impedance spectroscopy with the annealing time have also been made. These studies reveal that, with the increase in annealing time, there is a permanent gradual reduction in the d.c. resistance of annealed sensors. Further, it is also observed that with the increase in annealing time, the response time improves, barrier height reduces, barrier capacitance increases and the dependence of the sensitivity with temperature reduces while the sensitivity itself improves many-fold.

A Neuro-Fuzzy Classifier-Cum-Quantifier for Analysis of Alcohols and Alcoholic Beverages Using Responses of Thick-Film Tin Oxide Gas Sensor Array

A novel neuro-fuzzy classifier-cum-quantifier is presented. The proposed classifier retrieves both qualitative and quantitative information simultaneously from the steady-state responses of thick-film tin oxide gas sensor array when it was exposed to seven different kinds of alcohols and alcoholic beverages. The individual concentration bands were represented in the output feature space by fuzzy subsethood measure. The qualitative and quantitative classifications were done by training an artificial neural network (ANN) with backpropagation algorithm. Each output neuron of the network represented one out of the seven alcohols and alcoholic beverage classes and was trained to fire at the fuzzy subsethood value of the particular concentration band of a particular alcohol or alcoholic beverage whose sample was presented to the network. The proposed network gave satisfactory performance and simultaneous qualitative and quantitative classification of the alcohols and alcoholic beverages was obtained using a single neural network.

The effect of hydrogen-induced interface traps on a titanium dioxide-based palladium gate MOS capacitor (Pd-MOSC): a conductance study

Abstract The conductance versus gate voltage response of a palladium-gate MOS capacitor with 0.5 μm of TiO 2 (oxide layer) has been studied as a function of hydrogen gas concentration and signal frequency. The structure of the device was completed by evaporating titanium dioxide over p 〈111〉-type silicon wafer (cleaned as per standard silicon technology) having a resistivity of 3–5 Ω cm and subsequent palladium front with aluminium back metallization. The G - V response of the fabricated MOS capacitor was studied on exposure to hydrogen in Ar ambient. The fabricated device is sensitive to hydrogen (1–3%) at room temperature. The interface state density ( D it ) was determined at the surface potential corresponding to the peak in the conductance curve, using a bias scan conductance method at fixed frequency. It was found that D it increases with an increase in hydrogen gas concentration. Further, it has been observed that a change in conductance is better at lower frequencies, which may be due to the balanced communication of interface traps with the valance and conduction bands of silicon substrate.

Transformed cluster analysis: an approach to the identification of gases/odours using an integrated gas-sensor array

Abstract This paper deals with ‘transformed cluster analysis’, which can be used to analyse gas-sensor responses for the identification of individual gases/odours present in the ambient. The computational strategy for the analyses consists of transformation of data, graphical representation, formulation of the classification method, interpretation of results and quantification. The unknown gas/odour may be classified into five different probability groups according to the transformed responses obtained, i.e., real object, apparent object, probable object, doubt object and atypical object. This method also helps to evaluate the effect of dopants. The method is illustrated here with experimental data obtained in the laboratory. It is predicted that the technique can successfully be used for application-specific gas sensors.

Classification of Gases/Odors Using Dynamic Responses of Thick Film Gas Sensor Array

This paper proposes a new method for classification of gases/odors called average slope multiplication (ASM) using dynamic characteristics of thick film gas sensor array. The instantaneous values of the extracted dynamic response/recovery plots for various test gases viz., LPG, CCl4, CO, and C3H7OH were correlated to its neighboring response plots by the use of proposed ASM technique. It has been demonstrated that the proposed method offers excellent results for classification of gases/odors using the dynamic responses of thick film gas sensor array. Principal component analysis (PCA) has been further used for data preprocessing and dimensionality reduction. The extracted raw data, the ASM transformed data, and PCA preprocessed ASM data were trained and tested using the back-propagation neural network (BPNN). The results thus obtained have been studied and presented here. Cross validation scheme was adopted for all analysis. The BPNN trained and tested with raw data showed 86% classification accuracy, whereas the raw data after PCA preprocessing showed 90% classification. The ASM data showed 97% classification accuracy while ASM data with PCA preprocessing showed the best results giving 100% classification accuracy with duly trained BPNN. We therefore report that superior identification of gases/odors can be obtained using the dynamic response of gas sensor array with the proposed ASM method.

Sensitivity and response times of doped tin oxide integrated gas sensors

Abstract This paper deals with the sensitivity and responses of fabricated integral gas sensors using thick film technology. Four different sensors were fabricated on a single substrate using tin oxide as the parent material, and dopings were done using ZnO, MoO and CdS. It was observed that N 2 ambient is suitable as the background ambient for evaluation of characteristics of sensors. The fabricated sensors have shown varying sensitivity to hydrocarbons such as acetone, carbon tetrachloride, ethyl methyl ketone and xylene.

Fuzzy Entropy Based Neuro-Wavelet Identifier-Cum-Quantifier for Discrimination of Gases/Odors

In this paper, a new approach to design of an odor/gas identifier-cum-quantifier is presented. Dynamic response curves of an oxygen-plasma treated thick-film tin oxide sensor array exposed to four different gases were subjected to continuous wavelet transform (CWT). Appropriate wavelet coefficients were selected using multiscale principal component analysis (MSPCA). Fuzzy entropy and fuzzy subsethood values were calculated for the individual odor/gas and for the particular concentration band of each odor/gas, respectively. The quantitative information was encoded in the fuzzy subsethood values of the particular concentration bands in the output feature space, whereas the fuzzy entropy values were used to normalize the training data set consisting of MSPCA selected wavelet coefficients. A feedforward neural network was trained with a backpropagation algorithm with the training data containing the wavelet coefficients normalized with fuzzy entropies of individual odors/gases. The target data set was made up of the fuzzy subsethood values of the particular concentration band. The proposed network achieved identification and quantification of odors/gases with a 100% success rate. Also, fuzzy entropy based normalization helped to achieve 100% identification/quantification with a reduced number of sensors in the array.

Quantification of Individual Gases/Odors Using Dynamic Responses of Gas Sensor Array With ASM Feature Technique

This paper is a continuation of our previous work in which a new feature technique called average slope multiplication (ASM) was proposed to classify the individual gases/odors using dynamic responses of sensor array. The ASM method is used to quantify the individual gases/odors in this paper. Back propagation algorithm based two different neural network architectures (NNAs) called NNA1 and NNA2 are used to assess the ability of the ASM technique for quantification. The proposed method thus utilizes the newly developed feature method in the first stage and the specially designed neural quantifiers in the next subsequent stages. The ability of the proposed method has been insured by applying it on the published dynamic responses of the thick film gas sensor array. When the raw data were directly fed to the neural quantifiers, the results were 69% and 63% accurate for NNA1 and NNA2, respectively. The principal component analysis preprocessed version of raw data provided 74% and 67% quantification accuracy with the aforementioned architectures respectively. The performances of the ASM data were found to be 100% using both the network architecture without need of further preprocessing, with relatively less number of epochs and without any hidden layer. Thus, the proposed method can be utilized in electronic nose for classification/quantification purpose.