Behavior-Level Analysis of a Successive Stochastic Approximation Analog-to-Digital Conversion System for Multi-Channel Biomedical Data Acquisition

Tani, Shuntaro; Matsuoka, Toshimasa; Hirai, Yusaku; Kurata, Takeshi; Tatsumi, Keiji; Asano, Takao; Ueda, Mitsuru; Kamata, Toshihide

doi:10.1587/transfun.e100.a.2073

Cited by 3 publications

(10 citation statements)

References 24 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…According to the central limiting theorem, it can be assumed that the V os,i and V n,i follow a Gaussian distribution when the number of comparators N is large enough [18]. The standard deviation of the offset and noise σ tot can be estimated as follows [11]:…”

Section: Afe Ic and Implementation A Stochastic Flash Adcmentioning

confidence: 99%

“…In the sampling period, the switches connect the capacitors to input terminals (V IN ,P , V IN ,N ). In the conversion period, the capacitors are connected to V REF,P 11). Note that the V REF,P , V REF,N and V CM are generated by a band gap reference, regulators, and a resistive divider.…”

Section: Capacitor Dacmentioning

confidence: 99%

“…-ADCs [8], [9] are often used for high resolution applications. However, they are not easy to use in fast and multi-channel applications [10], [11]. Therefore, in order to achieve a high-resolution SAR-ADC, a digital error correction technique is important.…”

Section: Introductionmentioning

confidence: 99%

“…To break through the above issues, the novel ADC called successive-stochastic-approximation ADC (SSA-ADC) has been proposed [11], [12]. This architecture is based on the basic SAR-ADC and utilizes a stochastic flash ADC (SF-ADC) [13] to detect a small signal under noise level.…”

Section: Introductionmentioning

confidence: 99%

“…In this paper, the systemlevel implementation of the biomedical sensor system (especially ECG sensor) using the SSA-ADC is described, which is based on previous AFE IC implementation [12]. In addition, off-chip calibration based on machine learning algorithm is also described briefly based on the previous work [11].…”

Section: Introductionmentioning

confidence: 99%

See 4 more Smart Citations

A Biomedical Sensor System With Stochastic A/D Conversion and Error Correction by Machine Learning

Hirai¹,

Matsuoka

Tani

et al. 2019

IEEE Access

Self Cite

View full text Add to dashboard Cite

This paper presents a high-precision biomedical sensor system with a novel analog-frontend (AFE) IC and error correction by machine learning. The AFE IC embeds an analog-to-digital converter (ADC) architecture called successive stochastic approximation ADC. The proposed ADC integrates a stochastic flash ADC (SF-ADC) into a successive approximation register ADC (SAR-ADC) to enhance its resolution. The SF-ADC is also used as a digitally controlled variable threshold comparator to provide error correction of the SAR-ADC. The proposed system also calibrates the ADC error using the machine learning algorithm on an external PC without additional power dissipation at a sensor node. Due to the flexibility of the system, the design complexity of an AFE IC can be relaxed by using these techniques. The target resolution is 18 bits, and the target bandwidth (without digital low-pass filter) is about 5 kHz to deal with several types of biopotential signals. The design is fabricated in a 130-nm CMOS process and operates at 1.2-V supply. The fabricated ADC achieves the SNDR of 88 dB at a sampling frequency of 250 kHz by using the proposed calibration techniques. Due to the high-resolution ADC, the input-referred noise is 2.52 µV rms with a gain of 28.5 dB.

show abstract

Section: Afe Ic and Implementation A Stochastic Flash Adcmentioning

confidence: 99%

Section: Capacitor Dacmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

A Biomedical Sensor System With Stochastic A/D Conversion and Error Correction by Machine Learning

Hirai¹,

Matsuoka

Tani

et al. 2019

IEEE Access

Self Cite

View full text Add to dashboard Cite

show abstract

A Software Level Calibration Based on Bayesian Regression for a Successive Stochastic Approximation Analog-to-Digital Converter System

Tatsumi

Matsuoka

2019

IEEE Trans. Cybern.

View full text Add to dashboard Cite

Recently, a novel low-power high-precision analog-to-digital converter (ADC) called the successive stochastic approximation ADC has been proposed which has two kinds of outputs from different modes, and which requires a software-level error correction method of combining them into a high-precision total output. From the practical viewpoint, we propose an error correction method based on the Bayesian regression with an incremental learning, in which additional data are successively selected according to the uncertainty of the corresponding predictive total output, and the uncertainty is approximately estimated by evaluating the upper bound of the standard deviations of the Bayesian predictive distributions of the outputs in each block of a partition of the all data set. Through numerical experiments, we verify the performance of the proposed method.

show abstract

A Multi-Channel Biomedical Sensor System with System-Level Chopping and Stochastic A/D Conversion

HIRAI,

MATSUOKA,

KAMATA

et al. 2024

IEICE Trans. Fundamentals

View full text Add to dashboard Cite

This paper presents a multi-channel biomedical sensor system with system-level chopping and stochastic analog-to-digital (A/D) conversion technique. The system-level chopping technique extends the inputsignal bandwidth and reduces the interchannel crosstalk caused by multiplexing. The system-level chopping can replace an analog low-pass filter (LPF) with a digital filter and can reduce its area occupation. The stochastic A/D conversion technique realizes power-efficient resolution enhancement. A novel auto-calibration technique is also proposed for the stochastic A/D conversion technique. The proposed system includes a prototype analog front-end (AFE) IC fabricated using a 130 nm CMOS process. The fabricated AFE IC improved its interchannel crosstalk by 40 dB compared with the conventional analog chopping architecture. The AFE IC achieved SNDR of 62.9 dB at a sampling rate of 31.25 kSps while consuming 9.6 𝜇W from a 1.2 V power supply. The proposed resolution enhancement technique improved the measured SNDR by 4.5 dB.

show abstract

Behavior-Level Analysis of a Successive Stochastic Approximation Analog-to-Digital Conversion System for Multi-Channel Biomedical Data Acquisition

Cited by 3 publications

References 24 publications

A Biomedical Sensor System With Stochastic A/D Conversion and Error Correction by Machine Learning

A Biomedical Sensor System With Stochastic A/D Conversion and Error Correction by Machine Learning

A Software Level Calibration Based on Bayesian Regression for a Successive Stochastic Approximation Analog-to-Digital Converter System

A Multi-Channel Biomedical Sensor System with System-Level Chopping and Stochastic A/D Conversion

Contact Info

Product

Resources

About