Micromachining Error Tolerance Analysis in EEG Sensing Nanostructure Arrays: Control of Fano Resonance for Enhanced Performance

The silicon-based electroencephalography (EEG) sensing technology encounters performance limitations due to spectral distortions caused by micromachining, which significantly degrade its effectiveness in Brain-Computer Interface (BCI) systems. To enhance the performance of EEG sensors, this study systematically investigates the tolerance of micromachining errors in silicon-based nanostructured arrays, specifically focusing on photonic crystal nanobeam cavities (PCNCs) used in optical EEG sensing systems. We propose a comprehensive error control methodology that integrates finite-difference time-domain (FDTD) simulations with multivariate linear regression (MLR) analysis to quantitatively assess the impact of lithographic and alignment errors on Fano resonance spectral characteristics. Our analysis establishes critical tolerance boundaries: horizontal displacement errors must be maintained within −4.56 nm to +26.31 nm, and angular deviations should be constrained between −0.068° and +0.083°. By establishing precise tolerance boundaries, our approach effectively mitigates spectral distortion while enhancing production yield, thereby ensuring manufacturing consistency. Notably, this study aligns with the fundamental objective of precision allocation in conventional silicon nanocomponents: achieving target performance metrics with cost efficiency.