Substrate Optimization of Flexible Temperature Sensor for Wearable Applications

In flexible electronic devices and sensor fabrication, substrate selection is essential as it determines different parameters and techniques. This paper aims to find an efficient substrate selection approach for flexible and wearable electronic applications. A hybridized Multi-Criteria Decision-Making (MCDM) technique, namely the Analytic Hierarchy Tradeoff Ranking Technique (AH-TOR), is used to select the most suitable flexible substrate out of the several widely used options for the temperature sensor. The selection of the best substrate for the said application is made based on various properties. In this case, AHP is used to evaluate the criteria weights, while the TOR method determines the weights of alternatives. Further, different PEDOT: PSS-based temperature sensors have been fabricated to validate the optimization technique. Based on the performance of the sensors, it is found that the sensitivity of Polyethylene Terephthalate (PET) is superior, and the sensitivity of PET is approximately 53.3% better than Polydimethylsiloxane (PDMS) and 47.4% superior to Polyvinyl chloride (PVC). The result is in sync with the optimization, where PET is at the top among all other alternatives. This method will be helpful in large-area sensor fabrication and manufacturing of flexible electronic devices.

Automated summary

This paper investigates the importance of substrate selection in flexible electronic devices and sensor fabrication, and proposes an efficient approach based on a hybridized Multi-Criteria Decision-Making (MCDM) technique. The results of fabricating different temperature sensors validate the effectiveness of the method, with Polyethylene Terephthalate (PET) being identified as the best substrate.