Circuits-Informed Machine Learning Technique for Blind Open-Loop Digital Calibration of SAR ADC
This work presents a supervised machine-learning (ML) approach for blind digital calibration of SAR ADCs without requiring prior knowledge of errors. A low-speed reference ADC is used to train a shallow neural network (NN) to estimate errors in a high-speed ADC by comparing the outputs of the ADCs when their sampling instants align and subtracting these errors in the back-end. The proposed NN-calibration improves SFDR of a 28nm, 12-bit, 84MHz ADC by >38dB while consuming 25.8fJ/conversion-step.
Code (0)
등록된 구현이 없습니다.
Methods 이 논문이 사용한 방법론
Similar Papers 제목 키워드 기반
BUNET: Blind Medical Image Segmentation Based on Secure UNET
The strict security requirements placed on medical records by various privacy regulations become major obstacles in the age of big data. To ensure efficient machine learning as a service schemes while protecting data con…
Image SegmentationMedical Image SegmentationPrivacy PreservingSemantic SegmentationA Non-Recursive Space-Efficient Blind Approach to Find All Possible Solutions to the N-Queens Problem
N-Queen’s problem is the problem of placing N number of chess queens on an NxN chessboard such that none of them attack each other. A chess queen can move horizontally, vertically, and diagonally. So, the neighbours of…
AllN-Queens Problem - All Possible SolutionsAdaptive Quantum Physics-Informed Neural Networks for Differential Equations with Applications to Fluid Dynamics
Physics-informed neural networks (PINNs) have emerged as a versatile approach for solving nonlinear partial differential equations (PDEs), yet achieving high accuracy efficiently using these techniques remains challengin…
Qandle: Accelerating State Vector Simulation Using Gate-Matrix Caching and Circuit Splitting
To address the computational complexity associated with state-vector simulation for quantum circuits, we propose a combination of advanced techniques to accelerate circuit execution. Quantum gate matrix caching reduces t…
Scalable Quantum Error Mitigation with Neighbor-Informed Learning
Noise in quantum hardware is the primary obstacle to realizing the transformative potential of quantum computing. Quantum error mitigation (QEM) offers a promising pathway to enhance computational accuracy on near-term d…