Machine learning in electrical circuit simulation In the field of VLSI design, and specifically in digital circuits it is common to use electrical simulation software to find signal integrity issues.
I would like to use machine learning instead of running a true electrical simulation to do the same, in order to boost run time (simulations can be very heavy). For example, a NAND logic gate with 2 inputs and 1 output, and on each input there is a noise glitch, and I want to predict the glitch which may occur on the output. A glitch is a waveform represented as a series of voltage and time points. That is, for a given electrical circuit I would like to build a model which can be used to predict an output waveform given input waveforms. Instead of running a (spice) simulation code which calculates results analytically, the output waveform will be predicted or estimated by the model.
Training data can be generated plentifully by running real simulations as many as needed.
I need a direction for types of machine learning algorithms and architectures.
From my research I think using a graph-neural-network (GNN) could be a good direction since an electrical circuit is actually a graph, and it is important to capture the relationships between its elements and other graph properties.


Thanks!
For waves one usually uses a Sequence Model but for this purpose I think it will probably be the simplest to use a CNN. I am not very familiar with waveforms, but they seem to be similar to standing waves (time independent) or maybe even better as they also have scale invariance. Assuming the number of input waveforms are the same, I would simply stack them side by side and treat the result as any other image and do image classification using a CNN. You can probably also do the analysis by building a multi input CNN, but you probably have to build it from scratch, I don't know any samples of this kind of architecture available online.