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[资料] Machine Learning-based Design and Optimization of High-Speed Circuits @2024

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    The book systematically expounds the main results obtained by the author in the field of design and optimization of high-speed integrated circuits (ICs) and their standard blocks (heterogeneous ICs, analog-to-digital and digital-to-analog con- verters, input/output cells, etc.) operating in non-standard conditions (deviations of technological process parameters, supply voltage, ambient temperature, etc.). The proposed methods are based on machine learning and consider effects of different external and internal destabilizing factors (radiation exposure, self-heating, nonideality of the power source, input signals, interconnects, power rails, etc.).
Chapter 1 Means to Accelerate Transfer of Information Between Integrated Circuits
Chapter 2 Design Methods of Integrated Circuits, Working Under Non-standardOperatingConditions
Chapter 3 Signal Transmission Calibration Systems in Integrated Circuits
Chapter 4 Methods to Improve Linearity of Signal’s Analog-to-Digital Conversion with Self-Calibration
Chapter 5 Design of High-performance Heterogeneous Integrated Circuits
Chapter 6 Design of Digital Integrated Circuits by Improving the Characteristics of Digital Cells
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Machine Learning-based Design and Optimization of High-Speed Circuits @2024.pdf

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