Low power smart sensor circuits for biomedical applications : applications to neural interfaces

Health problem is one of the most important issues for our human race. In the past a few decades, smart sensors have been widely used in the healthcare area for real-time monitoring, diseases diagnosis and prevention, and a lot of research activities have been carried on in the design of low-cost, innovative biomedical smart sensors. Benefiting from the advances in the submicron complementary metal oxide semiconductor (CMOS) technologies, highly integrated smart sensor system have been developed for different applications. A smart sensor is a intelligent system which can be used to detect or predict problems. Typically it is composed of some basic blocks with function of signal acquisition, feature extraction and classification. This thesis focuses on the design of some basic circuit blocks in the smart sensor system for biomedical applications. We take the neural signal recording as an example, present a novel circuit design on the spike signal detection, feature extraction and classification. In this thesis, we proposed a novel low power, compact, current-mode spike detector (SPD) with feature extractor for real-time neural recording systems where neural spikes or action potential (AP)s are of interest. Such a circuit can enable massive compression of data facilitating wireless transmission. This design can generate a high signal-to-noise ratio (SNR) output by approximating the popularly used nonlinear nonlinear energy operator (NEO) through standard analog blocks. We show that a low pass filter after the NEO can be used for two functions (i) estimate and cancel low frequency interference and (ii) estimate threshold for spike detection. The circuit is implemented in a 65 nm CMOS process and occupies 200 μm x 150 μm of chip area. Operating from a 0.7 V power supply, it consumes about 30 nW of static power and 7 nW of dynamic power for 100 Hz input spike rate making it the lowest power consuming spike detector reported so far. After the detection, feature extraction is needed to get the neural spike shape information,

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