A massively parallel reverse modeling approach for semiconductor devices and circuits

We have developed a bi-directional neural network as a massively parallel computing architecture for the design of semiconductor devices and circuits. We call this operation reverse modeling since the neural network trained to model a circuit is used in a reverse direction. A feedforward neural network can be used to model the behavior of a system. It can quickly predict the system response of given input parameters. We have extended the applications of neural networks beyond their traditional roles of black box models. Our approach begins with a neural network trained to model the response of the circuit to design parameters. The reverse modeling is then carried out by applying a modified backpropagation learning rule to the trained network. This changes the multi-layer, feedforward neural network from a uni-directional model into a bi-directional model. In the forward direction, the neural network model predicts the circuit property from given design parameters. In the reverse direction, design parameters are synthesized from desired circuit properties. We have demonstrated this reverse modeling approach by designing an RF amplifier. A neural network was trained to model the relations between the matching circuit elements of the amplifier and its output characteristics (output power and power gain). The result model has an average error of 1.7%. The trained model was then used to synthesize matching circuits for 10 sets of desired output characteristics. The neural network synthesized circuits were simulated using LIBRA to verify their performance. The simulated circuit performance was, in average, within 2.6% of the desired characteristics