Low-Noise Micro-Power Amplifiers for Biosignal Acquisition

There are many different types of biopotential signals, such as action potentials (APs), local field potentials (LFPs), electromyography (EMG), electrocardiogram (ECG), electroencephalogram (EEG), etc. Nerve action potentials play an important role for the analysis of human cognition, such as perception, memory, language, emotions, and motor control. EMGs provide vital information about the patients which allow clinicians to diagnose and treat many neuromuscular diseases, which could result in muscle paralysis, motor problems, etc. EEGs is critical in diagnosing epilepsy, sleep disorders, as well as brain tumors. Biopotential signals are very weak, which requires the biopotential amplifier to exhibit low input-referred noise. For example, EEGs have amplitudes from 1 μV [microvolt] to 100 μV [microvolt] with much of the energy in the sub-Hz [hertz] to 100 Hz [hertz] band. APs have amplitudes up to 500 μV [microvolt] with much of the energy in the 100 Hz [hertz] to 7 kHz [hertz] band. In wearable/implantable systems, the low-power operation of the biopotential amplifier is critical to avoid thermal damage to surrounding tissues, preserve long battery life, and enable wirelessly-delivered or harvested energy supply. For an ideal thermal-noise-limited amplifier, the amplifier power is inversely proportional to the input-referred noise of the amplifier. Therefore, there is a noise-power trade-off which must be well-balanced by the designers. In this work I propose novel amplifier topologies, which are able to significantly improve the noise-power efficiency by increasing the effective transconductance at a given current. In order to reject the DC offsets generated at the tissue-electrode interface, energy-efficient techniques are employed to create a low-frequency high-pass cutoff. The noise contribution of the high-pass cutoff circuitry is minimized by using power-efficient configurations, and optimizing the biasing and dimension of the devices. Sufficient common-mode rejection ratio (CMRR) and power supply rejection ratio (PSRR) are achieved to suppress common-mode interferences and power supply noises. Our design are fabricated in standard CMOS processes. The amplifiers’ performance are measured on the bench, and also demonstrated with biopotential recordings

Wavelet-based EMG Sensing Interface for Pattern Recognition

Department of Electrical EngineeringAs interest in healthcare and smart devices has increased in recent years, the studies that are sensing and analyzing various bio signals, such as EMG, ECG, and EEG, have been growing. These studies and advances in smart devices have allowed human to increase access to their own physical information. With the physical information, human can diagnose himself or herself. These advances in technology will improve the quality of human life and provide solutions in various fields. The convergence of information and communication technologies has led to the fourth industrial revolution and the development of artificial intelligence, big data and the Internet of Things(IoT) by increasing computing power has led to various data analysis using machine learning. Various fields are moving toward the next level using machine learning, and this trend is also happening in the healthcare field. The era of self-diagnosis begins when medical knowledge, which had previously been entrusted to doctors is passed directly to consumers through big data and machine learning. Thanks to these developments, the healthcare interface, such as front-end integrated chip, is also working to leverage machine learning to deliver various solutions to consumers. Existing papers related to bio signals are focused on reducing power consumption, allowing long-term monitoring or reducing various noise. This paper provides an idea to extend the scope of data processes through machine learning while maintaining existing trends. Wavelet transform is implemented as a circuit to reduce computing power and eliminate specific frequency range including noise and motion artifact. The data from the chip is transmitted to external device (MATLAB) by wireless communication (Bluetooth) to be analyzed by machine learning. This paper present wavelet-based EMG sensing interface which includes front-end amplifier, wavelet filters, Analog to digital converter and Microcontroller. The main idea of the paper is front-end amplifiers which reduce a noise and motion artifact, wavelet filters that decompose the input signal for wavelet transform and machine learning for gesture recognition.ope

Low-Noise Energy-Efficient Sensor Interface Circuits

Today, the Internet of Things (IoT) refers to a concept of connecting any devices on network where environmental data around us is collected by sensors and shared across platforms. The IoT devices often have small form factors and limited battery capacity; they call for low-power, low-noise sensor interface circuits to achieve high resolution and long battery life. This dissertation focuses on CMOS sensor interface circuit techniques for a MEMS capacitive pressure sensor, thermopile array, and capacitive microphone. Ambient pressure is measured in the form of capacitance. This work propose two capacitance-to-digital converters (CDC): a dual-slope CDC employs an energy efficient charge subtraction and dual comparator scheme; an incremental zoom-in CDC largely reduces oversampling ratio by using 9b zoom-in SAR, significantly improving conversion energy. An infrared gesture recognition system-on-chip is then proposed. A hand emits infrared radiation, and it forms an image on a thermopile array. The signal is amplified by a low-noise instrumentation chopper amplifier, filtered by a low-power 30Hz LPF to remove out-band noise including the chopper frequency and its harmonics, and digitized by an ADC. Finally, a motion history image based DSP analyzes the waveform to detect specific hand gestures. Lastly, a microphone preamplifier represents one key challenge in enabling voice interfaces, which are expected to play a dominant role in future IoT devices. A newly proposed switched-bias preamplifier uses switched-MOSFET to reduce 1/f noise inherently.PHDElectrical EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttps://deepblue.lib.umich.edu/bitstream/2027.42/137061/1/chaseoh_1.pd

Low Power CMOS Chopper Preamplifier Based on Source-Degeneration Transconductors

This paper describes the design of a low-power, low-noise flicker CMOS chopper preamplifier for sensor signal conditioning. The core amplifier and the Gm-C output low pass filter of the proposed fully differential preamplifier are based on a source degeneration transconductor. The circuit was designed in a standard 0.18µm CMOS process with 1.8V supply voltage. It shows 42dB gain, 1 kHz bandwidth and a total power consumption of 84 µW. The proposed configuration achieves a noise efficiency factor of 4.6 and a total input-referred noise of 560 nVrms integrated from 0.1 to 1 kHz

A GaN-based wireless power and information transmission method using Dual-frequency Programmed Harmonic Modulation

Information transmission is often required in power transfer to implement control. In this paper, a Dual-Frequency Programmed Harmonic Modulation (DFPHM) method is proposed to transfer two frequencies carrying power and information with the single converter via a common inductive coil. The proposed method reduces the number of injection tightly coupled transformers used to transmit information, thereby simplifying the system structure and improving reliability. The performances of power and information transmission, and the method of information modulation and demodulation, as well as the principles of the control, are analyzed in detail. Then a simulation model is set up to verify the feasibility of the method. In addition, an experiment platform is established to verify that the single converter can transfer the power and information simultaneously via a common inductive coil without using tightly coupled transformers.Web of Science8498564984

Interface Circuits for Microsensor Integrated Systems

ca. 200 words; this text will present the book in all promotional forms (e.g. flyers). Please describe the book in straightforward and consumer-friendly terms. [Recent advances in sensing technologies, especially those for Microsensor Integrated Systems, have led to several new commercial applications. Among these, low voltage and low power circuit architectures have gained growing attention, being suitable for portable long battery life devices. The aim is to improve the performances of actual interface circuits and systems, both in terms of voltage mode and current mode, in order to overcome the potential problems due to technology scaling and different technology integrations. Related problems, especially those concerning parasitics, lead to a severe interface design attention, especially concerning the analog front-end and novel and smart architecture must be explored and tested, both at simulation and prototype level. Moreover, the growing demand for autonomous systems gets even harder the interface design due to the need of energy-aware cost-effective circuit interfaces integrating, where possible, energy harvesting solutions. The objective of this Special Issue is to explore the potential solutions to overcome actual limitations in sensor interface circuits and systems, especially those for low voltage and low power Microsensor Integrated Systems. The present Special Issue aims to present and highlight the advances and the latest novel and emergent results on this topic, showing best practices, implementations and applications. The Guest Editors invite to submit original research contributions dealing with sensor interfacing related to this specific topic. Additionally, application oriented and review papers are encouraged.

IUS/payload communication system simulator configuration definition study

The requirements and specifications for a general purpose payload communications system simulator to be used to emulate those communications system portions of NASA and DOD payloads/spacecraft that will in the future be carried into earth orbit by the shuttle are discussed. For the purpose of on-orbit checkout, the shuttle is required to communicate with the payloads while they are physically located within the shuttle bay (attached) and within a range of 20 miles from the shuttle after they have been deployed (detached). Many of the payloads are also under development (and many have yet to be defined), actual payload communication hardware will not be available within the time frame during which the avionic hardware tests will be conducted. Thus, a flexible payload communication system simulator is required