In this paper, disruptive research using modern embedding techniques and an attention-based encoder-decoder deep learning (DL) model is conducted to automate analog layout synthesis. Unlike previous legacy-based placement automation mechanisms, the attention-based Graph2Seq model is inherently independent of the number of devices within a circuit topology and their order. Moreover, its unsupervised training does not rely on expensive legacy layout data but only on sizing solutions. Experimental results show that the proposed model generates placement solutions at push-button speed and can generalize to circuit topologies and technological nodes not used in training. Moreover, while being scalable, the model produces placement solutions that compete with highly optimized analog placements and other, order-dependent and non-scalable, DL models.