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In this paper, a complete Lab-on-Chip (LoC) ion imaging platform for analysing Ion-Selective Membranes (ISM) using CMOS ISFET arrays is presented. An array of 128 × 128 ISFET pixels is employed with each pixel featuring 4 transistors to bias the ISFET to a common drain amplifier. Column-level 2-step readout circuits are designed to compensate for array offset variations in a range of up to ±1 V. The chemical signal associated with a change in ionic concentration is stored and fed back to a programmable gain instrumentation amplifier for compensation and signal amplification through a global system feedback loop. This column-parallel signal pipeline also integrates an 8-bit single slope ADC and an 8-bit R-2R DAC to quantise the processed pixel output. Designed and fabricated in the TSMC 180 nm BCD process, the System-on-Chip (SoC) operates in real time with a maximum frame rate of 1000 fps, whilst occupying a silicon area of 2.3 mm × 4.5 mm. The readout platform features a high-speed digital system to perform system-level feedback compensation with a USB 3.0 interface for data streaming. With this platform we show the first reported analysis and characterisation of ISMs using an ISFETs array through capturing real-time high-speed spatio-temporal information at a resolution of 16 μm in 1000 fps, extracting time-response and sensitivity. This work paves the way of understanding the electrochemical response of ISMs, which are widely used in various biomedical applications.

This work describes an array of 1024 ion-sensitive field-effect transistors (ISFETs) using sensor-learning techniques to perform multi-ion imaging for concurrent detection of potassium, sodium, calcium, and hydrogen. Analyte-specific ionophore membranes are deposited on the surface of the ISFET array chip, yielding pixels with quasi-Nernstian sensitivity to K+, Na+, or Ca2+. Uncoated pixels display pH sensitivity from the standard Si3N4 passivation layer. The platform is then trained by inducing a change in single-ion concentration and measuring the responses of all pixels. Sensor learning relies on offline training algorithms including k-means clustering and density-based spatial clustering of applications with noise to yield membrane mapping and sensitivity of each pixel to target electrolytes. We demonstrate multi-ion imaging with an average error of 3.7% (K+), 4.6% (Na+), and 1.8% (pH) for each ion, respectively, while Ca2+ incurs a larger error of 24.2% and hence is included to demonstrate versatility. We validate the platform with a brain dialysate fluid sample and demonstrate reading by comparing with a gold-standard spectrometry technique.

This paper presents a 32 × 32 ISFET array with in-pixel dual-sensing and programmability targeted for on-chip DNA amplification detection. The pixel architecture provides thermal and chemical sensing by encoding temperature and ion activity in a single output PWM, modulating its frequency and its duty cycle respectively. Each pixel is composed of an ISFET-based differential linear OTA and a 2-stage sawtooth oscillator. The operating point and characteristic response of the pixel can be programmed, enabling trapped charge compensation and enhancing the versatility and adaptability of the architecture. Fabricated in 0.18 $\mu \rm{m}$ standard CMOS process, the system demonstrates a quadratic thermal response and a highly linear pH sensitivity, with a trapped charge compensation scheme able to calibrate 99.5% of the pixels in the target range, achieving a homogeneous response across the array. Furthermore, the sensing scheme is robust against process variations and can operate under various supply conditions. Finally, the architecture suitability for on-chip DNA amplification detection is proven by performing Loop-mediated Isothermal Amplification (LAMP) of phage lambda DNA, obtaining a time-to-positive of 7.71 minutes with results comparable to commercial qPCR instruments. This architecture represents the first in-pixel dual thermo-chemical sensing in ISFET arrays for Lab-on-a-Chip diagnostics.

A novel microfluidic analysis platform is demonstrated for the real-time monitoring of traumatic brain injury (TBI) patients in intensive care. The system uses an on-chip array of Complementary Metal-Oxide-Semiconductor (CMOS) sensors implemented in an unmodified process to perform ion imaging. Specificity to several ionic species is achieved by depositing ion-selective membranes at the surface of the chip. Image processing techniques on the sensing array data enable the visualisation of the pattern of neurochemical changes caused by the evolving injury.