A Low-Power Fully Differential Reconfigurable Biomedical Electronics Interface to Detect Heart Signals

Microelectronics and Electronics（2010）

Inst. of Biomed. Eng. & Health Technol.

Cited 2|Views5

Abstract

A biomedical electronics interface to detect heart signals is presented including a reconflgurable full differential fifth-order Bessel Gm-C filter and a 12 bit low-power fully differential successive approximation register analog-to-digital converter (SAR ADC). The total fully differential structure reduces the input signal noise and distortion effectively. A switch array is used in Gm-C filter to realize three low cutoff frequencies for different biomédical signals processing. In SAR ADC, hybrid 9-bit charge-redistribution and 3-bit resistor binary-weighted DAC techniques and dynamic latch comparator are adopted to achieve optimization of resolution, area and power consumption. Fabricated in SMIC 0.18-μm 1P6M mixed-signal CMOS technology, the proposed biomédical electronics detecting interface only consume below 100μW under 1.8V supply voltage.

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Key words

CMOS integrated circuits,analogue-digital conversion,biomedical electronics,cardiology,comparators (circuits),digital-analogue conversion,distortion,filtering theory,flip-flops,low-power electronics,medical signal detection,medical signal processing,mixed analogue-digital integrated circuits,resistors,1P6M mixed-signal CMOS technology,3-bit resistor binary-weighted DAC,SAR ADC,analog-to-digital converter,biomedical signal processing,dynamic latch comparator,heart signals,hybrid 9-bit charge-redistribution,input signal distortion,input signal noise,low-power fully differential reconfigurable biomedical electronics interface,low-power fully differential successive approximation register,reconfigurable full differential fifth-order Bessel Gm-C filter,size 0.18 mum,voltage 1.8 V,Gm-C filter,biomedical electronics interface to detect heart signals,fully differential structure,low-power,reconflgurable,successive approximation register analog-to-digital converter (SAR ADC)

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Pretraining has recently greatly promoted the development of natural language processing (NLP)
We show that M6 outperforms the baselines in multimodal downstream tasks, and the large M6 with 10 parameters can reach a better performance
We propose a method called M6 that is able to process information of multiple modalities and perform both single-modal and cross-modal understanding and generation
The model is scaled to large model with 10 billion parameters with sophisticated deployment, and the 10 -parameter M6-large is the largest pretrained model in Chinese
Experimental results show that our proposed M6 outperforms the baseline in a number of downstream tasks concerning both single modality and multiple modalities We will continue the pretraining of extremely large models by increasing data to explore the limit of its performance

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