Electronic biosensors are a natural fit for field-deployable diagnostic devices because they can be miniaturized, mass produced, and integrated with circuitry. Unfortunately, progress in the development of such platforms has been hindered by the fact that mobile ions present in biological samples screen charges from the target molecule, greatly reducing sensor sensitivity. Under physiological conditions, the thickness of the resulting electric double layer is less than 1 nm, and it has generally been assumed that electronic detection beyond this distance is virtually impossible. However, a few recently described sensor design strategies seem to defy this conventional wisdom, exploiting the physics of electrical double layers in ways that traditional models do not capture. In the first strategy, charge screening is decreased by constraining the space in which double layers can form. The second strategy uses external stimuli to prevent double layers from reaching equilibrium, thereby effectively reducing charge screening. In this Perspective, we describe these relatively new concepts and offer theoretical insights into mechanisms that may enable electronic biosensing beyond the Debye length. If these concepts can be further developed and translated into practical electronic biosensors, we foresee exciting opportunities for the next generation of diagnostic technologies.

Going beyond the Debye Length: Overcoming Charge Screening Limitations in Next-Generation Bioelectronic Sensors

Data-intensive computing operations, such as training neural networks, are essential for applications in artificial intelligence but are energy intensive. One solution is to develop specialized hardware onto which neural networks can be directly mapped, and arrays of memristive devices can, for example, be trained to enable parallel multiply–accumulate operations. Here we show that memcapacitive devices that exploit the principle of charge shielding can offer a highly energy-efficient approach for implementing parallel multiply–accumulate operations. We fabricate a crossbar array of 156 microscale memcapacitor devices and use it to train a neural network that could distinguish the letters ‘M’, ‘P’ and ‘I’. Modelling these arrays suggests that this approach could offer an energy efficiency of 29,600 tera-operations per second per watt, while ensuring high precision (6–8 bits). Simulations also show that the devices could potentially be scaled down to a lateral size of around 45 nm. Arrays of memcapacitor devices that work via charge shielding can be used to implement artificial neural networks and could potentially offer an energy efficiency of 29,600 tera-operations per second per watt.

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Energy-efficient memcapacitor devices for neuromorphic computing

In recent years, there has been growing interest in developing capacitive biosensors using Complementary Metal Oxide Semiconductor (CMOS) technology for various life science applications including cellular growth monitoring, DNA hybridization detection, and drug tests. In this paper, we provide a comprehensive review of CMOS capacitive sensors featuring sensing electrodes connected to interface circuits. A capacitive biosensor is also incorporated with microfluidic structure. This paper addresses the challenges of developing high precision interface circuits for detecting small capacitance changes on sensing electrodes. Also, we discuss the practical issues in fabricating hermetic microfluidic packaging structures to direct the samples toward sensing sites and prevent the remaining part of circuitry from direct contact with the sample. By offering the advantages of low complexity and high precision, CMOS capacitive sensor arrays can be considered as an alternative candidate for high throughput screening of cellular and molecular activities.

CMOS based capacitive sensors for life science applications: A review

Electrical impedance spectroscopy has been used extensively for sensing and biosensing due to the multiple electrical properties that can be interrogated through varying the frequency of the electrical excitation. In this paper, we review the basic concepts and key issues for applying impedance spectroscopy in sensing and biosensing, with emphasis on the development of precise, low-cost and portable instruments. An impedance spectroscopy system can be divided into three parts: the signal processing unit, the sensing unit and the data analysis unit. Herein, we focus on the signal processing unit, responsible for generating the excitation signal and performing the impedance readout. Special attention is given to small and low-cost signal processing circuits, which are essential for portability and point-of-care diagnosis. We also elaborate upon the various methods to fabricate the sensing units, including the choice of nanomaterials and biomolecules in controlled molecular architectures. From an instrumentation perspective, we discuss possible sources of interference in the measurement protocols. When impedance spectroscopy measurements are performed with arrays of sensing units, as with electronic tongues, large amounts of data are generated. This has motivated an increasing use of statistical and computational methods for data analysis. We present some of these methods, with examples of information visualization and machine learning techniques, which have been employed in analyzing impedance spectroscopy data in recent years.

Roadmap for Electrical Impedance Spectroscopy for Sensing: A Tutorial

This letter presents a large-scale fully integrated multimodal sensor array for biological imaging. The $512\times 256$ sensor array can perform spatially resolved electrochemical impedance spectroscopy (EIS) with switching frequencies up to 100 MHz, acquire multicolor optical images, and sense pH using titanium nitride (TiN) ion sensitive field effect transistors (ISFETs). The chip features code-division multiplexed (CDM) readout of groups of pixels simultaneously, enabling extended integration times at a given frame rate. The system is implemented in 180-nm CMOS with 9.5 $\mu \text{m}\,\,\times11.5\,\,\mu \text{m}$ pixels. Its overall fill factor is 57%, including peripheral control and readout circuits, yielding a wide-field spatially resolved multimodal biosensing platform for advanced cell culture applications.

A Large-Scale Multimodal CMOS Biosensor Array With 131,072 Pixels and Code-Division Multiplexed Readout

We describe the realization of a fully electronic label-free temperature-controlled biosensing platform aimed to overcome the Debye screening limit over a wide range of electrolyte salt concentrations. It is based on an improved version of a 90-nm CMOS-integrated circuit featuring a nanocapacitor array, readout and A/D conversion circuitry, and a field programmable gate array (FPGA)-based interface board with NIOS II soft processor. We describe chip's processing, mounting, microfluidics, temperature control system, as well as the calibration and compensation procedures to reduce systematic errors, which altogether make up a complete quantitative sensor platform. Capacitance spectra recorded up to 70 MHz are shown and successfully compared to predictions by finite element method (FEM) numerical simulations in the Poisson–Drift–Diffusion formalism. They demonstrate the ability of the chip to reach high upper frequency of operation, thus overcoming the low-frequency Debye screening limit at nearly physiological salt concentrations in the electrolyte, and allowing for detection of events occurring beyond the extent of the electrical double layer. Furthermore, calibrated multifrequency measurements enable quantitative recording of capacitance spectra, whose features can reveal new properties of the analytes. The scalability of the electrode dimensions, interelectrode pitch, and size of the array make this sensing approach of quite general applicability, even in a non-bio context (e.g., gas sensing).

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Cecilia Laborde

Papers

A CMOS Pixelated Nanocapacitor Biosensor Platform for High-Frequency Impedance Spectroscopy and Imaging