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We demonstrate an organic smart label electronic system using p-type organic thin film transistors (OTFT) for temperature sensing applications. The electronic label consists of all organic temperature sensor, memory, logic and interface circuits and detects whether the critical temperature threshold value has been exceeded and records the data digitally in writeonce-read-many (WORM) form that can be transmitted to a reader through wireless communication. A comparator is used to interface the sensor to the logic part. The logic circuit block processes and bundles the sensor information along with the necessary additional information that is required for a successful wireless transmission. We have demonstrated the operation of the reported organic smart label system using a silicon based modulator/rectifier circuit for RF communication. The organic logic circuit was built using standard cell design approach with approximately 180 p-type OTFTs. All the circuits were operated with a of -20 V.

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Smart sensors are an integral part of the Fourth Industrial Revolution and are widely used to add safety measures to human–robot interaction applications. With the advancement of machine learning methods in resource-constrained environments, smart sensor systems have become increasingly powerful. As more data-driven approaches are deployed on the sensors, it is of growing importance to monitor data quality at all times of system operation. We introduce a smart capacitive sensor system with an embedded data quality monitoring algorithm to enhance the safety of human–robot interaction scenarios. The smart capacitive skin sensor is capable of detecting the distance and angle of objects nearby by utilizing consumer-grade sensor electronics. To further acknowledge the safety aspect of the sensor, a dedicated layer to monitor data quality in real-time is added to the embedded software of the sensor. Two learning algorithms are used to implement the sensor functionality: (1) a fully connected neural network to infer the position and angle of objects nearby and (2) a one-class SVM to account for the data quality assessment based on out-of-distribution detection. We show that the sensor performs well under normal operating conditions within a range of 200 mm and also detects abnormal operating conditions in terms of poor data quality successfully. A mean absolute distance error of 11.6mm was achieved without data quality indication. The overall performance of the sensor system could be further improved to 7.5mm by monitoring the data quality, adding an additional layer of safety for human–robot interaction.