  Analog and mixed-signal (AMS) circuit designs still rely on human design
expertise. Machine learning has been assisting circuit design automation by
replacing human experience with artificial intelligence. This paper presents
TAG, a new paradigm of learning the circuit representation from layouts
leveraging text, self-attention and graph. The embedding network model learns
spatial information without manual labeling. We introduce text embedding and a
self-attention mechanism to AMS circuit learning. Experimental results
demonstrate the ability to predict layout distances between instances with
industrial FinFET technology benchmarks. The effectiveness of the circuit
representation is verified by showing the transferability to three other
learning tasks with limited data in the case studies: layout matching
prediction, wirelength estimation, and net parasitic capacitance prediction.
