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Nanopore technology has been extensively investigated for analysis of biomolecules, and a success story in this field concerns DNA sequencing using a nanopore chip featuring an array of hundreds of biological nanopores (BioNs). Solid-state nanopores (SSNs) have been explored to attain longer lifetime and higher integration density than what BioNs can offer, but SSNs are generally considered to generate higher noise whose origin remains to be confirmed. Here, we systematically study low-frequency (including thermal and flicker) noise characteristics of SSNs measuring 7 to 200 nm in diameter drilled through a 20-nm-thick SiN x membrane by focused ion milling. Both bulk and surface ionic currents in the nanopore are found to contribute to the flicker noise, with their respective contributions determined by salt concentration and pH in electrolytes as well as bias conditions. Increasing salt concentration at constant pH and voltage bias leads to increase in the bulk ionic current and noise therefrom. Changing pH at constant salt concentration and current bias results in variation of surface charge density, and hence alteration of surface ionic current and noise. In addition, the noise from Ag/AgCl electrodes can become predominant when the pore size is large and/or the salt concentration is high. Analysis of our comprehensive experimental results leads to the establishment of a generalized nanopore noise model. The model not only gives an excellent account of the experimental observations, but can also be used for evaluation of various noise components in much smaller nanopores currently not experimentally available.

Nanopore technology holds great promise for a wide range of applications such as biomedical sensing, chemical detection, desalination, and energy conversion. For sensing performed in electrolytes in particular, abundant information about the translocating analytes is hidden in the fluctuating monitoring ionic current contributed from interactions between the analytes and the nanopore. Such ionic currents are inevitably affected by noise; hence, signal processing is an inseparable component of sensing in order to identify the hidden features in the signals and to analyze them. This Guide starts from untangling the signal processing flow and categorizing the various algorithms developed to extracting the useful information. By sorting the algorithms under Machine Learning (ML)-based versus non-ML-based, their underlying architectures and properties are systematically evaluated. For each category, the development tactics and features of the algorithms with implementation examples are discussed by referring to their common signal processing flow graphically summarized in a chart and by highlighting their key issues tabulated for clear comparison. How to get started with building up an ML-based algorithm is subsequently presented. The specific properties of the ML-based algorithms are then discussed in terms of learning strategy, performance evaluation, experimental repeatability and reliability, data preparation, and data utilization strategy. This Guide is concluded by outlining strategies and considerations for prospect algorithms.