Contact and sheet resistance of SnO2 thin films from transmission-line model measurements

Hoefer, Ulrich; Steiner, Klaus; Wagner, Elmar

doi:10.1016/0925-4005(94)01557-x

Cited by 48 publications

(18 citation statements)

References 8 publications

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“…Crystalline metal oxide layers can be modeled by a combination of different parallel RC elements representing the electrode-layer interface, the grain boundaries, and the metal oxide grains, respectively [58]. These show different influences of the interaction between the gas-sensitive layer and the gas components [19,59,60], which can be probed by measuring the complex impedance over a wide frequency range. The resulting EI spectrum allows separation of the different contributions and thus extraction of features which can be used for gas classification and quantification [56].…”

Section: Electrical Impedance Spectroscopymentioning

confidence: 99%

Sensor Arrays, Virtual Multisensors, Data Fusion, and Gas Sensor Data Evaluation

Reimann

Schütze

2013

Springer Series on Chemical Sensors and Biosensors

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Selectivity will remain one of the main challenges for gas sensors. The majority of single gas sensors have a broad response spectrum and additional effort is required to provide specific information from the recorded raw data. Sensor arrays comprising a number of different sensors, either of the same basic type or combining different sensor principles, are often employed to improve the selectivity. Another approach is the dynamic operation of a single sensor element leading to a virtual multisensor. Depending on the application, both classification, i.e., recognition of a certain gas or gas mixture, and quantification, i.e., determining the concentration of a target gas, are required. For evaluation of the higher-dimensional data vectors obtained from the sensor system, different methods are commonly used, e.g., unsupervised and supervised multivariate statistics or artificial neural networks. For comprehensive classification, a multistep process is employed, typically comprising data preprocessing, feature extraction, dimensionality reduction, and finally classification. This chapter addresses typical methods used for gas sensor data evaluation and also briefly addresses challenges relating to sensor calibration and drift compensation. Four application examples are shown in detail demonstrating the use of different sensor configurations and data processing approaches: robust ozone quantification, sweat odor assessment trained by correlation with a human sensory panel, fire detection in buildings, and finally underground fire detection in coal mines requiring very high selectivity.

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Section: Electrical Impedance Spectroscopymentioning

confidence: 99%

Sensor Arrays, Virtual Multisensors, Data Fusion, and Gas Sensor Data Evaluation

Reimann

Schütze

2013

Springer Series on Chemical Sensors and Biosensors

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“…26,27 used an array of electrodes of differing width and separation to examine contact resistance effects in tin dioxide sensors. The transmission-line method they used involves measuring the total resistance of a semiconductor sample as a function of electrode separation.…”

Section: Electrode Geometrymentioning

confidence: 99%

Electrode materials and electrode-oxide interfaces in semiconductor gas sensors

Lee

2013

Semiconductor Gas Sensors

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“…18). Transmission line measurements (TLM) performed with thick SnO 2 layers exposed to CO and NO 2 did not result in values of R C clearly distinguishable from the noise [63], whereas in the case of dense thin films the existence of R C was proved [64]. Again, the relative importance played by different terms may be influenced by the presence of reducing gases because one can expect different effects for grain/grain interfaces compared with electrode/grain interfaces.…”

Section: Sensing Process and Signal Transductionmentioning

confidence: 99%

Chemical Sensors Based on Catalytic Reactions

Bârsan

Schierbaum

2008

Handbook of Heterogeneous Catalysis

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The sections in this article are Introduction Definitions and Classifications Typical Examples Calorimetric Sensors Electronic Conductance Sensors Sensing Process and Signal Transduction Modeling the Sensor Response Role of Contacts Materials Tin Oxide (SnO 2 ) Tungsten Oxide ( WO 3 ) Chromium Titanium Oxide ( Cr 2− x Ti x O 3 ) Gallium Oxide ( Ga 2 O 3 ) Solid‐State Electrochemical Sensors Conclusions

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Contact and sheet resistance of SnO2 thin films from transmission-line model measurements

Cited by 48 publications

References 8 publications

Sensor Arrays, Virtual Multisensors, Data Fusion, and Gas Sensor Data Evaluation

Sensor Arrays, Virtual Multisensors, Data Fusion, and Gas Sensor Data Evaluation

Electrode materials and electrode-oxide interfaces in semiconductor gas sensors

Chemical Sensors Based on Catalytic Reactions

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