Convolutional neural networks for extracting non-linear compact models

A convolutional neural network (CNN) for extracting non-linear compact model parameters for semiconductor devices, the CNN comprising:

• an input layer configured to receive input data that is sample points representing electrical characteristics of a semiconductor device;
• one or more convolution layers configured to process the input data, creating processed data, and detect local patterns and extract features, creating extracted features, wherein the local patterns detected by the convolution layers are not image features but are instead electrical characteristics of electronic devices;
• one or more pooling layers down-sampling the processed data to reduce spatial dimensions while retaining important features;
• one or more fully connected layers configured to perform high-level reasoning and decision-making based on the extracted features;
• and
• an output layer configured to generate predictions for parameters of a non-linear compact model.

A system for extracting non-linear compact model parameters for semiconductor devices, the system comprising:

• a memory configured to store input data that is sample points representing electrical characteristics of a semiconductor device;
• and
• a processor configured to execute instructions stored in the memory, the instructions comprising:
• processing the input data through one or more convolution layers of a CNN, creating processed data, to detect local patterns and extract features, creating extracted features, wherein the local patterns detected by the convolution layers are not image features but are instead electrical characteristics of electronic devices;
• down-sampling the processed data through one or more pooling layers of the CNN to reduce spatial dimensions while retaining important features;
• passing an output of the pooling layers through one or more fully connected layers of the CNN to perform high-level reasoning and decision-making based on the extracted features;
• and
• generating predictions for parameters of a non-linear compact model using an output layer of the CNN.

A non-transitory computer-readable medium storing instructions that, when executed by a processor, perform a method of extracting non-linear compact model parameters for semiconductor devices using a convolutional neural network (CNN), the method comprising:

• receiving input data representing electrical characteristics of a semiconductor device;
• processing the input data through one or more convolution layers of the CNN, creating processed data, to detect local patterns and extract features, creating extracted features, wherein the local patterns detected by the one or more convolution layers are not image features but are instead electrical characteristics of electronic devices;
• down-sampling the processed data through one or more pooling layers of the CNN to reduce spatial dimensions while retaining important features;
• passing an output of the pooling layers through one or more fully connected layers of the CNN to perform high-level reasoning and decision-making based on the extracted features;
• and
• generating predictions for parameters of a non-linear compact model using an output layer of the CNN.