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Abstract While the electrical models of the membrane-based solid-state nanopores have been well established, silicon-based pyramidal nanopores cannot apply these models due to two distinctive features. One is its 35.3° half cone angle, which brings additional resistance to the moving ions inside the nanopore. The other is its rectangular entrance, which makes calculating the access conductance challenging. Here, we proposed and validated an effective transport model (ETM) for silicon-based pyramidal nanopores by introducing effective conductivity. The impact of half cone angle can be described equivalently using a reduced diffusion coefficient (effective diffusion coefficient). Because the decrease of diffusion coefficient results in a smaller conductivity, effective conductivity is used for the calculation of bulk conductance in ETM. In the classical model, intrinsic conductivity is used. We used the top-down fabrication method for generating the pyramidal silicon nanopores to test the proposed model. Compared with the large error (≥25% in most cases) when using the classical model, the error of ETM in predicting conductance is less than 15%. We also found that the ETM is applicable when the ratio of excess ion concentration and bulk ion concentration is smaller than 0.2. At last, it is proved that ETM can estimate the tip size of pyramidal silicon nanopore. We believe the ETM would provide an improved method for evaluating the pyramidal silicon nanopores. 

Label-free nanopore sensors have emerged as a new generation technology of DNA sequencing and have been widely used for single molecule analysis. Since the first α-hemolysin biological nanopore, various types of nanopores made of different materials have been under extensive development. Noise represents a common challenge among all types of nanopore sensors. The nanopore noise can be decomposed into four components in the frequency domain (1/f noise, white noise, dielectric noise, and amplifier noise). In this work, we reviewed and summarized the physical models, origins, and reduction methods for each of these noise components. For the first time, we quantitatively benchmarked the root mean square (RMS) noise levels for different types of nanopores, demonstrating a clear material-dependent RMS noise. We anticipate this review article will enhance the understanding of nanopore sensor noises and provide an informative tutorial for developing future nanopore sensors with a high signal-to-noise ratio.