A Multi-Surface Acoustic Wave Sensor platform for the Detection and Identification of Toxic Gases

Mariem Ben Hadj Abdallah (1) , Christine Mer (2) , Jean-Philippe Poli (3) , Franck Badets (4) , Edwin Friedmann (5) , Venceslass Rat (6) , Thierry Laroche (7) , Samuel Saada (8)

1 LIMTEC (CEA, LIST) - Laboratoire Instrumentation Multi-TEChnique (CEA, LIST)
2 LCIM (CEA, LIST) - Laboratoire Capteurs et Instrumentation pour la Mesure (CEA, LIST)
3 DIN (CEA, LIST) - Département d'instrumentation Numérique (CEA, LIST)
4 DCOS - Département Composants Silicium
5 LIACI (CEA, LIST) - Laboratoire Intelligence Artificielle de Confiance pour l’Instrumentation (CEA, LIST)
6 DSYS - Département Systèmes
7 SOITEC SA, Crolles
8 DM2I (CEA, LIST) - Département Métrologie Instrumentation & Information (CEA, LIST)

In this paper, we present a platform based on a modular approach coupled with powerful algorithms for accurate detection and identification of chemical compounds. The system relies on multi-SAW (surface Acoustic Wave) sensors that are functionalized differently, resulting in multi-responses that collectively constitute the fingerprint of the chemical compound. A prototype has been developed and the overall system, including the design of SAW module, the acquisition system, learning algorithms and online recognition of various compounds, has been tested and validated. The results showed a reliable and accurate system with a perfect score of 100\% recognition of DMMP.

Keywords: Saw sensors; chemical sensors; toxic gases detection; recognition algorithms

Domains: Electronics; Material chemistry; Machine Learning [stat.ML]

Mariem Ben Hadj Abdallah, Christine Mer, Jean-Philippe Poli, Franck Badets, Edwin Friedmann, et al.. A Multi-Surface Acoustic Wave Sensor platform for the Detection and Identification of Toxic Gases. 2023 IEEE SENSORS, Oct 2023, Vienna (AUSTRIA), Austria. pp.1--4, ⟨10.1109/SENSORS56945.2023.10324973⟩. ⟨cea-04487892⟩