A dual-slope capacitance-to-digital converter for pressure-sensing is presented and demonstrated in a complete microsystem. The design uses base capacitance subtraction with a configurable capacitor bank to narrow down input capacitance range and reduce conversion time. An energy-efficient iterative charge subtraction method is proposed, employing a current mirror that leverages the 3.6 V battery supply available in the system. We also propose dual-precision comparators to reduce comparator power while maintaining high accuracy during slope conversion, further improving energy efficiency. The converter occupies 0.105 mm ² in 180 nm CMOS and achieves 44.2 dB SNR at 6.4 ms conversion time and 110 nW of power, corresponding to 5.3 pJ/conv-step FoM. The converter is integrated with a pressure transducer, battery, processor, power management unit, and radio to form a complete 1.4 mm × 2.8 mm × 1.6 mm pressure sensor system aimed at implantable devices. The multi-layer system is implemented in 180 nm CMOS. The system was tested for resolution in a pressure chamber with an external 3.6 V supply and serial communication bus, and the measured resolution of 0.77 mmHg was recorded. We also demonstrated the wireless readout of the pressure data on the stack system operating completely wirelessly using an integrated battery.

This article presents a voice and acoustic activity detector that uses a mixer-based architecture and ultra-low-power neural network (NN)-based classifier. By sequentially scanning 4 kHz of frequency bands and down-converting to below 500 Hz, feature extraction power consumption is reduced by 4x. The NN processor employs computational sprinting, enabling 12x power reduction. The system also features inaudible acoustic signature detection for intentional remote silent wakeup of the system while re-using a subset of the same system components. The measurement results achieve 91.5%/90% speech/non-speech hit rates at 10-dB SNR with babble noise and 142-nW power consumption. Acoustic signature detection consumes 66 nW, successfully detecting a signature 10 dB below the noise level.

Acoustic sensing is one of the most widely used sensing modalities to intelligently assess the environment. In particular, ultra-low power (ULP) always-on voice activity detection (VAD) is gaining attention as an enabling technology for IoT platforms. In many practical applications, acoustic events-of-interest occur infrequently. Therefore, the system power consumption is typically dominated by the always-on acoustic wakeup detector, while the remainder of the system is power-gated the vast majority of the time. A previous acoustic wakeup detector [1] consumed just 12nW but could not process voice signals (up to 4kHz bandwidth) or handle non-stationary events, which are essential qualities for a VAD. Prior VAD ICs [2,3] demonstrated reliable performance but consumed significant power (>20μW) and lacked an analog frontend (AFE), which further increases power. Recent analog-domain feature extraction-based VADs [4,5] also reported μW-level power consumption, and their simple decision tree [4] or fixed neural network-based approach [5] limited broader use for various acoustic event targets. In summary, no sub-μW VAD has been reported to date, preventing the use of VADs in unobtrusive mm-scale sensor nodes.

Small form-factor piezoresistive MEMS sensors, often configured in a Wheatstone bridge, are widely used to measure physical signals such as pressure [1-3], temperature [4], force [1], and gas concentration. A common method to realize a digital output from the bridge involves biasing the bridge with a DC voltage source and using a low-noise amplifier followed by an ADC. While a bridge measurement can achieve high resolution and linearity, it is very power hungry [3] because the bridge resistance is low (typically 1–10kΩ). Both the high power and high instantaneous current make it unsuitable as a sensing interface in miniaturized microsystems with battery capacities of <10μAh and ∼15kΩ internal resistance [5]. Duty cycled excitation was proposed in [1] to reduce power in moderate dynamic range (DR) applications, lowering bridge excitation energy by up to 125x compared to static biasing. However, the excitation energy consumption (∼250nJ) is still much larger than the interface circuit conversion energy, and therefore limits overall sensor energy efficiency. To address this challenge, we propose an energy-efficient highly duty-cycled excitation bridge-sensor readout circuit for small battery-operated systems. Due to high battery resistances, the excitation voltage (VEX) is sourced from an on-chip decoupling capacitance that drops ∼100mV during excitation and then slowly recharges from the battery. To avoid accuracy degradation from this voltage fluctuation, the design samples not only the inputs (VIN+/−) but also VEX, from which it generates a DAC reference voltage (VDAC). We also propose an offset calibration and input-range matching method. We demonstrate operation of the bridge-to-digital converter (BDC) integrated with a complete and fully functional pressure-sensing system, including a processor, battery, power management unit, RF transmitter, and optical receiver.