Ultra-Fast Flash-ADC Design Automation Approach Using Artificial Neural Networks: Achieving Design inUnder One Second

The growing demand for high-performance analog circuits requires innovative design methodologies that accelerate the designprocess while maintaining accuracy and reliability. Machine Learning (ML) techniques have recently emerged in IntegratedCircuit (IC) design, leveraging their powerful modeling capabilities across different design stages. This paper introducesa simulation-free design automation methodology using Artificial Neural Networks (ANNs) to enhance Flash-ADC designworkflows. The proposed approach adopts a top-bottom hierarchical design strategy: ANNs replace simulators and designers,eliminating the need for time-consuming simulations or excessive design iterations. To demonstrate the method, an 8-bit Flash-Analog-to Digital Converter (ADC) was synthesized. The results show that the proposed framework significantly reducescomputational overhead and accelerates the design process, achieving ultra-fast within less than one second. The approachcan be generalized for ADC design and a hybrid testbench setup for analog optimization, offering a scalable solution for othercomplex systems.