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.

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In RF transmitter (TX) IC having an offset‐QPSK modulation scheme, a sine wave of the transmission signal represented by a 12‐bit digital code generated through the genetic algorithm reduces the I/Q imbalance and the intermodulation distortion in the analog circuit of the TX. It was experimentally confirmed by using test ICs fabricated in a 130‐nm CMOS process. © 2016 Wiley Periodicals, Inc. Microwave Opt Technol Lett 58:2905–2912, 2016