Optimizing Sensor Geometry for Enhanced Performance

Abstract

Introduction / Objective: Sensor geometry affects signal-to-noise ratio, sensitivity, and mechanical stress distribution. Traditional designs struggle to balance sensitivity and durability. This research explores thin-film and multilayer configurations and shape variations to optimize geometry for improved SNR, sensitivity, and mechanical reliability. Methods: Mathematical modeling and simulation analyze the impact of geometry on sensor performance. Thin-film and multilayer designs influence Signal-to-Noise Ratio (SNR) and sensitivity. A constraint analysis was performed to assess the impact of shape optimization, comparing circular and rectangular geometries. Performance indicators were analyzed to evaluate trade-offs among sensitivity, noise, and mechanical reliability. Results: Thin-film sensors exhibit greater sensitivity and a higher signal-to-noise ratio as the thickness is reduced. Multilayer designs increase sensitivity but decrease the Signal-to-Noise Ratio (SNR) due to noise amplification. Circular geometries offer a more uniform stress distribution than rectangular geometries, reducing the risk of local stress concentrations and structural failures. Geometry optimization improved SNR by reducing noise-sensitive areas and strengthening signalgenerating regions. discussion: Geometry optimization significantly improved SNR by minimizing noise-prone areas and enhancing signal-generating regions. Discussion: The results indicate that sensor geometry significantly impacts performance, with thinfilm designs offering a high Signal-to-Noise Ratio (SNR) and multilayer configurations improving sensitivity while requiring noise control. Circular shapes increase durability by reducing stress concentrations. Systematic geometric optimization could lead to improvements in electrical and mechanical performance. Future research will focus on experimental validation and adaptation to real-world environments, such as vibration and temperature. conclusion: The study demonstrates that geometry and material selection are crucial for sensor optimization, impacting SNR, sensitivity, and durability. The findings provide insights applicable to high-precision industries such as aerospace, automotive, and medical devices, emphasizing the potential of geometry optimization in enhancing sensor performance. Conclusion: This study highlights the importance of geometry and materials in optimizing sensors, affecting signal-to-noise ratio, sensitivity, and durability. The results, based on numerical simulations, will be validated through prototype fabrication and experimental testing to ensure their practical applicability.