composed of a CMOS chip, an integrated emission filter (see refs. [49,50] for example in­

tegrated filters for CMOS fluorescence sensors), DNA probes, and a fluidic cap. The CMOS

integrated circuit itself included an array of 32 × 32 fluorescence detection biosensing

elements.

Each sensing element included an n-well/p-sub photodiode, a first-order current sen­

sing modulator and all its required electronics, and a heater for DNA amplification

procedures such as PCR. Each biosensing element was 100 μm × 100 μm, and the pho­

todetector was 50 μm × 50 μm. The current sensing modulator was a ΣΔ operator that

enhanced the noise performance significantly. As a proof of concept, the authors de­

monstrated the successful detection of a panel of human upper respiratory viruses.

6.6 Conclusions

In this chapter, we discussed several trends in the development of microsystems for

bioelectronics. Specifically, we reviewed CMOS circuits and some of their salient design

aspects as they pertain to the development of neural interfaces, electrochemical sensors,

interfacial capacitance, cell impedance sensors, and image sensors. The devices we dis­

cussed all had a common denominator: their engineering featured the confluence of

several fields (e.g., biochemistry, microsystems engineering, cell biology, to name a few).

This illustrates the multi-disciplinary nature of the bioelectronics field and the need for a

co-design approach that leverages insights from various technological and scientific do­

mains to provide viable solutions to the many problems addressed.

In closing, we will point the reader to two additional trends in bioelectronics. The first,

which is apparent from some of the exemplary devices we discussed, is the drive towards

multi-modal systems that can support two or more sensing modalities on the same

platform. Such multi-modal devices will enable new capabilities in biospecies analysis

by providing orthogonal sensing capabilities that offer a more complete view of the

microenvironments under study. The second trend is the increasing use of big data and

machine learning techniques to gain additional insights from measured sensor data. We

envision that bioelectronics hardware will be rendered more effective when combined

with the power of machine learning algorithms capable of identifying and classifying

signal patterns that are consistent with biophysical or biochemical cues of interest.

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