Published Papers
IEICE TRANS. FUNDAMENTALS, VOL.E107-A, NO.8 AUGUST 2024
A Multi-Channel Biomedical Sensor System with
System-Level Chopping and Stochastic A/D Conversion
https://www.jstage.jst.go.jp/article/transfun/E107.A/8/E107.A_2023EAP1093/_article/-char/ja
SUMMARY
This paper presents a multi-channel biomedical sensor system with system-level chopping and stochastic analog-to-digital (A/D) conversion techniques. 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 uW from a 1.2Vpower supply. The proposed resolution enhancement technique improved the measured SNDR by 4.5 dB.IEEE Access, Vol. 7, pp. 21990-22001, Feb. 2019
A Biomedical Sensor System With Stochastic A/D
Conversion and Error Correction by Machine Learning
https://ieeexplore.ieee.org/document/8636959
SUMMARY
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 μVrms with a gain of 28.5 dB.IEICE TRANSACTIONS on Fundamentals of Electronics,
Communications and Computer Sciences. 2017, E100-A(10), p. 2073-2085
Behavior-Level Analysis of a Successive Stochastic
Approximation Analog-to-Digital Conversion System for Multi-Channel
Biomedical Data Acquisition
https://ir.library.osaka-u.ac.jp/repo/ouka/all/65064/IEICE%20TRANSACTIONS%20on%20Fundamentals_E100-A_10_2073.pdf
SUMMARY
In the present paper, we propose a novel high-resolution analog-to-digital converter (ADC) for low-power biomedical analog frontends, which we call the successive stochastic approximation ADC. The proposed ADC uses a stochastic flash ADC (SF-ADC) to realize a digitally controlled variable-threshold comparator in a successive-approximationregister ADC (SAR-ADC), which can correct errors originating from the internal digital-to-analog converter in the SAR-ADC. For the residual error after SAR-ADC operation, which can be smaller than thermal noise, the SF-ADC uses the statistical characteristics of noise to achieve high resolution. The SF-ADC output for the residual signal is combined with the SAR-ADC output to obtain high-precision output data using the supervised machine learning method.IEEE NEWCAS 2017
An Analog Front-End Employing 87 dB SNDR
Stochastic SAR-ADC for a Biomedical Sensor
https://ieeexplore.ieee.org/document/8010165
SUMMARY
The present paper introduces a novel ADC for biomedical sensors that embeds successive-approximationregister ADC and stochastic flash ADC operations. The ADC in the analog-front-end IC fabricated in a 130-nm CMOS process demonstrated 87 dB SNDR for a 20.5 Hz input signal at an oversampling rate of 250 kS/s with calibration by a supervised machine learning technique.IEEE BioCAS 2016
A programmable controller for spatio-temporal
pattern stimulation of cortical visual prosthesis
https://ieeexplore.ieee.org/document/7833824
Wiley Microwave and Optical Technology Letters, Vol.58,
Issue 12, Dec 2016
A Study on Performance Improvement of RF
Transmitter IC using Genetic Algorithm
https://onlinelibrary.wiley.com/doi/epdf/10.1002/mop.30180
IEEE EMBC 2015
Cortical neural excitations in rats in vivo with using a
prototype of a wireless multi-channel microstimulation system
https://ieeexplore.ieee.org/document/7318690
Far East Journal of Electronics and Communications. 2014,
13(2), p. 91-97
Digital Implementation of Third Harmonic Distortion
Reduction in Fourth-order ΔΣ D/A Converter
https://ir.library.osaka-u.ac.jp/repo/ouka/all/51734/FEJEC13_2_91.pdf
IEICE Technical Report, Vol. 114, No. 346, pp. 73-78,
Nov. 2014
Application of Stochastic A/D Conversion to
SAR-ADC
https://ken.ieice.org/ken/paper/20141201yBUX/eng/