Ultra Tiny Neural Network for Accurate Pressure Sensors Under Multiple Thermal Stresses

MEMS pressure sensors are widely used in various application fields such as industrial, consumer, medical, and automotive. However, cost-effective manufacturing techniques severely affect these sensors due to temperature, dust, humidity, and many other stresses which ultimately lead to drift in their measurements. Thermal stresses are the main cause of sensor drifts. Exposure to high levels of temperatures can cause output deviations in the sensor's response for hours (short term) and several days (long term). This paper studied a state of art MEMS pressure sensor under 3 simulated case studies representing real-world application scenarios. The first case study was dedicated to the drifts caused by the soldering process during assembly with five reflow cycles. The second one induced drift due to the exposure of the sensor to a thermal stress of 150 Celsius degrees for 1,000 hours. The last case represented an aging test for the sensor left at the ambient temperature of 25 Celsius degree. Pressure measurements were acquired by several real devices and compared to the pressure measured by a golden reference barometer. In each case study, data was categorized into 3 bins to achieve more accurate compensations. The pressure error datasets served to train an ultra-tiny convolutional neural network to predict the pressure error for the compensation. The network decreased the absolute pressure errors of the sensors by 84.13% in average after 5 soldering cycles. The model was composed of 101 parameters with a total FLASH memory size of 13 KiB, including all required application libraries. Computational complexity was 146 multiply-accumulate operations. It was deployed on a tiny off-the-shelf MCU (STM32U5, 160MHz) achieving an inference time of 50 μ s.