Variation resolutions for CMOS sensing networks

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proposed circuits can be combined with other techniques including high-Q impedance matching for input voltage boosting and hierarchical tandem stages for further improvement on operating conditions. With a Q=10 matching network, -27 dBm sensitivity and 22% efficiency can be achieved for about 0.5 V DC output to a 500 kΩ load at 570 MHz. On the other hand, the sensing variation can originate from the targeted biological sensing signal, which complicates both the sensor system design and the associated signal analysis. We will illustrate a spike-sorting method to reliably classify the enteric neural signals which have unique waveform features but large variation in magnitude, timing and duration. The proposed fastDTW spike classification algorithm provides improvements in accuracy and computational cost in comparison with Cross-correlation based template matching and PCA + k-means clustering without time warping. When appled to mouse ENS neurons in high noise and high variability environment, fastDTW successfully recognized spikes with variability is as large as 1.2 ms in width and a few milli-volt in magnitude. The captured waveform features are used for variation correlation analyses to better understand the operating principles of enteric nervous system. Although other variation sources can also affect the sensor system design, our approaches of device compensation based on operational feedback and signal tolerance based on time warping are able to give illustrations for sensor designers to successfully countermeasure uncontrollable variation sources.

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Internet of Things; Neuron network; RF energy harvesting; Spike sorting; Electrical engineering


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Union Local


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Kan, Edwin Chihchuan

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Lal, Amit
Molnar, Alyosha Christopher

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Electrical and Computer Engineering

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Ph. D., Electrical and Computer Engineering

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Doctor of Philosophy

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dissertation or thesis

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