Deep Neural Network Regression‐Assisted Pressure Sensor for Decoupling Thermal Variations at Different Operating Temperatures

Decoupling environment‐dependent response in sensing techniques is essential for the diverse practical applications. This work presents a novel thermal effect decoupling method for sponge pressure sensors based on a deep neural network (DNN) regression model, which is difficult to achieve owing to the material‐ and structure‐related complex effects of the sponge‐based pressure sensor. A poly(3,4‐ethylenedioxythiophene):poly(styrenesulfonate)‐based multifunctional device is fabricated with a both pressure and thermally responsive part and an only thermally responsive part; and a DNN model with two input features is adapted to implement the substantial pressure prediction system without thermal interference. Proposed model shows the robust decoupled pressure‐sensing capability with high accuracy of ≈96.23% using two input features. It also enables accurate pressure prediction under both the thermally steady and transition regions, which indicates significant potential for a precise measurement system. These results demonstrate the possibility of reliable pressure monitoring under varying thermal conditions, which is important for accurately measuring pressure in complex power plants, human–machine interfaces, and compact wearable platforms. The efficient measurement of pressure using a sensor under various environmental conditions, such as temperature and humidity, remains challenging due to electrical distortions. Herein, a sponge‐based pressure sensor is developed as a system‐on‐chip decoupling system, and a novel decoupling system is proposed to separate thermal effects from the pressure sensor using a deep neural network‐based regression model.