{'fraction_non_alphanumeric': 0.011433597185576077, 'fraction_numerical': 0.0008795074758135445, 'mean_word_length': 5.654970760233918, 'pattern_counts': {'":': 0, '<': 0, '<?xml version=': 0, '>': 0, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
Investigation of the enhancement of microelectromechanical capacitive pressure sensor performance using the genetic algorithm optimization technique

The electrical nonlinearity in microelectromechanical system (MEMS) capacitive pressure sensors is highly related to the geometry of the movable parts of these capacitors. Decreasing the electrical nonlinearity is one of the vital requirements to enhance the performance of these types of sensor, besides the high sensitivity. In the current investigation, the profile of the upper surface of the movable part of the sensor is optimally designed to achieve this goal. This profile is depicted by a fourth order polynomial to give the genetic algorithm the flexibility to change the polynomial coefficients to achieve the objectives. The MATLAB® code bvp4c is used to numerically integrate the governing differential equations. A COMSOL finite element model is created to calculate the flexible electrode deflection and the sensor capacitance for validation purposes. Finally, a weighted objective function is proposed and a guide for the recommended ranges of weight values is presented.