An on-chip test structure to measure the Seebeck coefficient of thermopile sensors

Thermopile sensors have a wide range of applications in consumer and industry. Seebeck coefficient is a basic thermal parameter of thermopile sensors. Extracting the Seebeck coefficient of both materials and thermocouple in thermopile sensors is of great importance. In this work, an on-chip test structure is designed. It consists of a substrate, a framework, supporting legs and a sensitive region which has a resistor serving as both heater and temperature detector. A set of on-chip test structures are fabricated along with a thermopile sensor. Its measurement results are analyzed and compared with apparatus measurement results. These results are consistent with each other, and the validity of structure is verified.

Privacy-Preserving Sensing and Two-Stage Building Occupancy Prediction Using Random Forest Learning

Sensing and predicting occupancy in buildings is an important task that can lead to significant improvements in both energy efficiency and occupant comfort. Rich data streams are now available that allow for machine learning-based algorithm implementation of direct and indirect occupancy estimation. We evaluate ensemble models, namely, random forests, on data collected from an 8 × 8 PIR matrix thermopile sensor with the dual goal of predicting individual cell temperature values and subsequently detecting the occupancy status. Evaluation of the method is based on a real case study deployed in an IT Hub in Bucharest, for which we have collected over three weeks of ground data, analyzed, and used it in order to predict occupancy in a room. Results show a 2–4% mean absolute percentage error for the temperature prediction and > 99% accuracy for a three-class model to detect human presence. The resulting outputs can be used by predictive building control models to optimize the commands to various subsystems. By separating the specific deployment from the system architecture and data structure, the application can be easily translated to other usage profiles and built environment entities. As compared to vision-based systems, our solution preserves privacy with improved performance when compared to single PIR or indirect estimation.

Improving the Irradiance Data Measured by Silicon-Based Sensors

Silicon-based sensors are widely used for monitoring solar irradiance, in particular, in the field of Photovoltaic (PV) applications. We present a method to correct the global horizontal irradiance measured by silicon-based sensors that reduces the difference to the standard thermopile sensor measurements. A major motivation to use silicon-based sensors for the measurements of irradiance is their lower cost. In addition, their response time is much lower, and their spectral response is much closer to that of the PV systems. The analysis of the differences is based on evaluating four parameters that influence the sensor measurements, namely the temperature, cosine error, spectral mismatch, and calibration factor. Based on the analysis, a correction model is applied to the silicon sensors measurements. The model separates measurements under a clear sky and cloudy sky by combining the clearness index and the solar zenith angle. By applying the correction model on the measurements of the silicon-based sensor, the differences between sensor readings have been reduced significantly. The relative root mean squared difference (rRMSD) between the daily solar irradiation measured by both sensors decreased from 10.6% to 5.4% after applying the correction model, while relative mean absolute difference (rMAD) decreased from 7.4% to 2.5%. The difference in total annual irradiation decreased from 70 KWh/m2 (6.5%) to 15 kWh/m2 (1.5%) by the correction. The presented correction method shows promising results for a further improvement in the accuracy of silicon-based sensors.

A Novel Infrared Temperature Measurement with Dual Mode Modulation of Thermopile Sensor

Superior to the traditional infrared temperature sensing architecture including infrared sensor and thermistor, we propose a novel sensing approach based on a single thermopile sensor with dual modes modulation. A switching and sensing circuit is proposed and realized with a chopper amplifier AD8551 and p-channel MOSFET (PMOS) for switching between detection of thermal radiation and the target and the ambient temperature for compensation. The error of target temperature after temperature compensation is estimated at less than 0.14 °C.

Bi2Te3 and Sb2Te3 Thin Films with Enhanced Thermoelectric Properties for Flexible Thermal Sensors

The influence of substrate type in boosting thermoelectric properties of co-evaporated Bi2Te3 and Sb2Te3 films (with 400 nm-thick) is here reported. Optimized power factor values are 2.7 × 10−3 W K−2 m−1 and 1.4 × 10−3 W K−2 m−1 for flexible Bi2Te3 and Sb2Te3 films, respectively. This is an important result as it is at least 2 times higher than the power factor found in the literature for flexible Bi2Te3 and Sb2Te3 films. A flexible infrared thermopile sensor was developed with high detectivity (2.50 × 107 cm √HzW−1).