Increasing the Selectivity of Pt-Gate SiC Field Effect Gas Sensors by Dynamic Temperature Modulation

Abstract

Abstract-Based on a diode coupled silicon carbide field effect transistor with platinum as catalytic gate material, the influence of dynamic temperature modulation on the selectivity of GasFETs has been investigated. This operating mode, studied intensively for semiconductor gas sensors, has only recently been applied to field effect transistors. A suitable temperature cycle (T-cycle) for detection of typical exhaust gases (CO, NO, C 3 H 6 , H 2 , NH 3 ) was developed and combined with appropriate signal processing. The sensor data was evaluated using multivariate statistics, e.g. linear discriminant analysis (LDA). Measurements have proven that typical exhaust gases can be discriminated in backgrounds with 0%, 10% and 20% oxygen. Furthermore, we are able to quantify the mentioned gases and to determine unknown concentrations based on training data. Very low levels of relative humidity (r.h.) below a few percent influence the sensor response considerably but for higher levels the cross interference of humidity is negligible. In addition, experiments regarding stability and reproducibility were performed.

Selective detection of naphthalene with nanostructured WO<sub>3</sub> gas sensors prepared by pulsed laser deposition

“…The resulting signals, after pre-processing and feature extraction, were analyzed by linear discriminant analysis (LDA), a multivariate pattern recognition method which separates different classes of input data while trying to group data sets of the same assigned group (Klecka, 1980;Gutierrez-Osuna, 2002). The combination of TCO and LDA has shown to improve selectivity and sensitivity of gas sensors, both MOS sensors (Gramm et al, 2003;Meier et al, 2007;Reimann and Schütze, 2012;Leidinger et al, 2014) and other types, e.g., GasFET devices (Bur et al, 2012).…”

Discrimination and quantification of volatile organic compounds in the ppb-range with gas sensitive SiC-FETs using multivariate statistics

“…The operating temperature of the sensor was modulated, i.e., run in temperature cycled operation, TCO, in order to increase the selectivity of the sensor [15], [24]. The chosen temperature cycle consists of three temperature plateaus, i.e.…”

“…In temperature cycled operation pre-processing, in particular normalization of the data, is done on a cycle-bycycle basis. It was reported that setting the cycle mean to one is an effective method to reduce multiplicative drift [24]. However, it should be noted that when setting the mean of each cycle to one, information regarding discrimination of different gases which is contained in the raw data close to the mean of the cycle, is discarded.…”

“…In order to reduce the dimension of the input data, feature extraction is applied. Especially, features describing the shape of the sensor response, e.g., mean values and slopes (here: line of best fit), are very effective for discrimination [24], [27], [28]. In fact, it was reported that these features outperform Fourier and Wavelet transformation coefficients which are typically extracted from periodic signals [29].…”

“…In order to enhance the performance of SiC-FET sensors dynamic operation, i.e., temperature cycled operation, TCO, can be used [24]. Temperature modulation gives rise to several advantages [25], among others the transient response of the sensor provides an unique response pattern or signature for each gas thereby increasing the selectivity.…”