A 2.5-GS/s 12-bit four-way time-interleaved pipelined-SAR ADC is presented in 28-nm CMOS. A bias-enhanced ring amplifier is utilized as the residue amplifier to achieve high bandwidth and excellent power efficiency compared with a traditional operational amplifier. A high linearity front-end is proposed to alleviate the non-linearity of the diode for ESD protection in the input PAD. The embedded input buffer can suppress the kickback noise at high input frequencies. A blind background calibration based on digital-mixing is used to correct the mismatches between channels. Additionally, an optional neural network calibration is also provided. The prototype ADC achieves a low-frequency SNDR/SFDR of 51.0/68.0 dB, translating a competitive FoMw of 0.48 pJ/conv.-step at 250 MHz input running at 2.5 GS/s.
This paper proposes a background calibration scheme for the pipelined‐Successive Approximation Register (SAR) Analog‐to‐Digital Converter (ADC) based on the neural network. Due to the non‐linear function fitting capability of the neural network, the linearity of the ADC is improved effectively. However, the hardware complexity of the neural network limits its application and promotion in ADC calibration. Hence, this paper also presents the optimization schemes, including the neuron‐based sharing neural network and the partially binarized with fixed neural network, in terms of calibration architecture and algorithm. A 60 MS/s 14‐bit pipelined‐SAR ADC prototyped in 28‐nm technology is utilized to verify the feasibility of the proposed calibration method. The measurement results show that the proposed calibration greatly enhances the Spurious Free Dynamic Range (SFDR) and Signal‐to‐Noise‐and‐Distortion Ratio (SNDR) from low frequency to Nyquist frequency. Meanwhile, the original calibrator and improved calibrator are synthesized in Synopsys Design Compiler to compare their hardware complexity. Compared with the unoptimized version, the optimized schemes can decrease the logic area and the network weights up to 76% and 52%, with negligible loss in calibration performance.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.