Investigation on Ultra-miniature and Ultra-low-power Non-invasive CMOS pH Sensor for Intracellular Monitoring

The intrinsic multilayer process and the dense transistor capacity of complementary metal oxide semiconductor (CMOS) technology has offered a great compact solution for miniaturized noninvasive pH sensors. Existing solutions to intracellular sensing are mainly through invasive approaches, which will...

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Bibliographic Details
Main Author: Zou, Xingyu
Other Authors: Han, Ruonan
Format: Thesis
Published: Massachusetts Institute of Technology 2022
Online Access:https://hdl.handle.net/1721.1/139149
Description
Summary:The intrinsic multilayer process and the dense transistor capacity of complementary metal oxide semiconductor (CMOS) technology has offered a great compact solution for miniaturized noninvasive pH sensors. Existing solutions to intracellular sensing are mainly through invasive approaches, which will likely damage the target cell. However, learning what is going on inside the cell while keeping the cell alive and safe is highly demanded. Currently, no non-invasive pH sensors have been demonstrated for intracellular activity monitoring yet, mainly due to the constraints in device size and power consumption. This thesis designs a non-invasive fully-scalable CMOS pH sensor with a diameter smaller than 30 𝜇m and a power consumption of 23 nW using TSMC 65 nm CMOS technology, targeting the application for intracellular activity monitoring. This thesis focuses on the design of a pH sensing pixel and its signal processing unit, which has the potential to work with different communication and powering units. We conduct simulations to show that our sensor node can sense pH information from the environment, and the sensed data can be encoded and transformed into a digital waveform that carries the pH information. Simulation results have also showed that our sensor is very promising for intracellular sensing, and it can help expand our understanding for intracellular activities. Moreover, this design is fully-scalable, so it can be easily adopted in a more advanced technology node, which can potentially lead to extensive biomedical applications.