Predicting the number of contacts and dimensions of full-custom integrated circuit blocks using neural network techniques

Block layout dimension prediction is an important activity in many very large scale integration computer-aided design tasks, among them structural synthesis, floor planning and physical synthesis. Block layout dimension prediction is harder than block area prediction and has been previously considered to be intractable. The authors present a solution to this problem using a neural network machine learning approach. The method uses a neural network to predict first the number of contacts; then another neural network uses this prediction and other circuit features to predict the width and the height of its layout. The approach has produced much better results than those published-a dimension (aspect ratio) prediction average error of less than 18% with a corresponding area prediction average error of less than 15%. Furthermore, the technique predicts the number of contacts in a circuit with less than 4% error on average