利用深度强化学习的SAR ADC量化误差优化方法OA

In the design of successive approximation register type analog-to-digital converters(SAR ADCs),non-ideal factors such as capacitor mismatch,comparator misalignment,and noise disturbances lead to increased quantization errors and reduce effective bits in behavioral level simulations,which seriously affect the setting of ADC design parameters.To address these issues,a method of dynamic strategy optimization based on reinforcement learning is proposed.The complete error model of non-ideal factors for ADC performance is constructed,and the deep reinforcement learning algorithm(Deep Q-Network)is used to dynamically adjust and optimize design parameters.By taking a typical 18 bit two-stage SAR ADC as the research object,the dynamic mapping law of quantization error corresponding to the full dynamic range of ADC input voltage from 0.000 004 V to 0.999 996 V(covering 24 999 equidistant voltage testing points)is deeply explored,providing a standardized and directly callable parameter configuration paradigm for the hardware implementation of SAR ADC,and providing practical theoretical reference and data support for the engineering implementation of high-precision analog-to-digital conversion systems.