AIPlace: Analog IC Placement with Multi-Task Learning Framework
Lijie Wang, Jing Wang, Song Chen, Qi Xu · 2025
Layout design of analog integrated circuits is a time-consuming manual process with limited automation methods. Recently, advances in machine learning have opened up possibilities for automated design, making it a viable option to improve efficiency. In this paper, we present an innovative and highly effective approach to achieve automated analog circuit placement. We transform the analog placement constraints into multiple task objectives, and apply multi-task neural network learning to perform accurate placement solutions efficiently. Besides, the global position information is utilized to achieve more orderly placement. Due to the computational properties of the network, the method exhibits versatility in accommodating diverse scales of circuit netlists. Moreover, the model is trained through unsupervised learning. Compared to the supervised counterpart using many generated synthetic layout datasets, the proposed approach dramatically reduces the cost of placement data. Experimental results demonstrate that compared to SOTA works, the proposed placement learning method can achieve significant performance gains.