Ajit Sharma

The Resonating Star Gyroscope: A Novel Multiple-Shell Silicon Gyroscope With Sub-5 deg/hr Allan Deviation Bias Instability

We report on the design, fabrication and characterization of a novel multiple-shell silicon vibratory microgyroscope. The resonating star gyroscope (RSG) is formed as a merged superposition of two square shells, yielding in-plane flexural modes that are utilized to sense rotation along the normal axis. The first prototypes of the single-shell RSG were implemented with 65 mum thick trench-refilled polysilicon structural material using the HARPSS process. These devices exhibited open-loop rate sensitivity of approximately 800 muV/deg/s. Despite high-aspect ratio sensing gaps, the device yielded poor sensitivity caused by low resonant-mode quality factors. To alleviate the Q TED losses caused by the inevitable formation of voids in trench-refilled structural material, the RSG was implemented in (111) single crystalline silicon. A 2.5-mm multiple-shell RSG was fabricated in 40 mum-thick SOI device layer using a simple two-mask process. Multiple-shells enable a higher operating frequency and larger resonant mass, essential components for reducing the mechanical noise floor of the sensor. Experimental data of a high-Q (111) multiple-shell prototype indicates sub-5 deg/hr Brownian noise floor, with a measured Allan deviation bias drift of 3.5 deg/hr. The gyroscope exhibits an open-loop rate sensitivity of approximately 16.7 mV/deg/s in vacuum.

Multi-modal smart bio-sensing SoC platform with >80dB SNR 35µA PPG RX chain

A multi-modal analog front end (AFE) and ultra-low energy bio-sensing CMOS SoC is presented. System/ circuit techniques enable signal path duty cycles as low as sub-1% and result in a 35μA Photo Plethysmography (PPG) RX Chain - 5X lower than published state of the art - while maintaining overall SNR > 80dBFS. The signal chain is adaptively synchronized by an ultra-low power FSM and includes a 1.3μW 14b 1kSPS SAR A/D. Input signal-aware, real-time data path adaptation is achieved by leveraging on-the-fly algorithms running on an external microcontroller (μC) to further reduce system energy. A programmable, asynchronous capacitive reset amplifier (PARCA) with NEF of 4.8 and dx/dt analog feature extractor demonstrate energy efficient ECG capture. A battery-powered, Bluetooth low energy (BLE) based, wearable platform with simultaneous ECG and PPG acquisition using this AFE has been demonstrated.

Data acquisition for wearables and in-patient monitoring

In-patient monitoring of blood pressure, heart rate, and pulse oximetry require very sensitive analog front ends (AFEs) designed from a system perspective. Analog techniques to lower power consumption of the AFEs while maintaining SNR will be discussed. Challenges for power management in wearables and digital processing needed for next generation patient monitoring will be discussed from the context of improving patient comfort, without sacrificing performance. This paper describes some of the critical parameters and design approaches for state-of-the-art monitoring systems focusing on signal acquisition and intelligent signal processing.

Circuits and systems for energy efficient smart wearables

Wearable devices and the associated push to smart health management have become an important facet of the Internet of Things. Wearable technology — with its diverse use cases, signal transduction mechanisms and unique software/processing requirements — is an ideal lens through which to study the broader Internet of Things. This paper investigates the unique challenges in wearable technology by using optical heart rate monitoring as an example. Strategies encompassing process technology, devices, circuits, systems and algorithms are leveraged to achieve the trifecta of good performance, low power and small form factor.

Smart sensing for IoT applications

Large scale deployment of sensing nodes for Internet of Things (IoT) applications will be an energy constrained system. To improve the energy efficiency in such systems, making sensors more intelligent will be an important tool. In this paper, we will discuss three emerging trends for smart sensing: (1) Integration of sensing elements with low-power in-situ signal processing on the same chip or package, (2) Integration of multiple sensing modalities on the same chip or package to provide more useful data to the end application, and (3) Compressive sensing techniques to extract the useful information from raw sensor output.