A Power-Efficient Capacitive Read-Out Circuit with Parasitic-Cancellation for MEMS Cochlea Sensors



Wang S, Koickal TJ, Hamilton A, Mastropaolo E, Cheung R, Abel A, Smith L & Wang L (2016) A Power-Efficient Capacitive Read-Out Circuit with Parasitic-Cancellation for MEMS Cochlea Sensors. IEEE Transactions on Biomedical Circuits and Systems, 10 (1), pp. 25-37.

This paper proposes a solution for signal read-out in the MEMS cochlea sensors that have very small sensing capacitance and do not have differential sensing structures. The key challenge in such sensors is the significant signal degradation caused by the parasitic capacitance at the MEMS-CMOS interface. Therefore, a novel capacitive read-out circuit with parasitic-cancellation mechanism is developed; the equivalent input capacitance of the circuit is negative and can be adjusted to cancel the parasitic capacitance. Chip results prove that the use of parasitic-cancellation is able to increase the sensor sensitivity by 35 dB without consuming any extra power. In general, the circuit follows a low-degradation low-amplification approach which is more power-efficient than the traditional high-degradation high-amplification approach; it employs parasitic-cancellation to reduce the signal degradation and therefore a lower gain is required in the amplification stage. Besides, the chopper-stabilization technique is employed to effectively reduce the low-frequency circuit noise and DC offsets. As a result of these design considerations, the prototype chip demonstrates the capability of converting a 7.5 fF capacitance change of a 1-Volt-biased 0.5 pF capacitive sensor pair into a 0.745 V signal-conditioned output at the cost of only 165.2 μW power consumption. © 2015 IEEE.

Capacitive read-out; chopper-stabilization; low capacitance measurement; MEMS cochlea; parasitic-cancellation; sensor interface

IEEE Transactions on Biomedical Circuits and Systems: Volume 10, Issue 1

Publication date29/02/2016
Publication date online26/03/2015
Date accepted by journal31/12/2014

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Professor Leslie Smith

Professor Leslie Smith

Emeritus Professor, Computing Science

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