AnGeL: Fully-Automated Analog Circuit Generator Using a Neural Network Assisted Semi-Supervised Learning Approach

In this work, we first present a database including labeled and unlabeled data. We use NN to determine the behavior of complicated topologies by combining the more simple ones. Using this database, we propose a fully-automated analog circuit generator framework, AnGeL. AnGeL performs all the schematic circuit design steps from deciding the circuit topology to determining the circuit parameters. Our results show that for multiple circuit topologies, in comparison to the state-of-the-art works while maintaining the same accuracy, the required labeled data is reduced by 4.7x -1090x. Also, the runtime of AnGeL is 2.9x -75x faster.

💾 https://ieeexplore.ieee.org/document/10190116 (запейволлено, см. 1й комментарий к посту)

PS: вот и до аналоговых дизайнеров добралась эта вечеринка 🎉
@embedoka