## Current Analysis and Electromagnetic Computation
These two steps are from EMSim and are intended to subsequently produce training data for GANs.
## Feature Extraction
Feature extraction aims to extract cell current, power grid and EM information from the database of the chip physical layout.
Then, we convert them into feature maps.
### Cell Current Map
```
create_current_map.py
optional arguments:
[ --def_path ] path to the def file, should end in .def
[ --current_path ] path to the simulated logic cell currents
[ --num_input_stimuli ] number of plaintexts
[ --target_area_x ] target simulated area in x axial direction
[ --target_area_y ] target simulated area in y axial direction
[ --num_probe_x_tiles ] number of point grid in x axial direction
[ --num_probe_y_tiles ] number of point grid in y axial direction
[ --layout_min_x ] Reference coordinate in x axial direction
[ --layout_min_y ] Reference coordinate in y axial direction
[ --start_points ] Start of sample point for each current trace
[ --sample_points ] End of sample point for each current trace
[ --current_map_train ] generate cell current map for GAN training
[ --current_map_test ] generate cell current map for EM prediction
```
### Power Grid Map
```
create_grid_map(2-pad).py
optional arguments:
[ --metal_layers ] target metal layers
[ --target_area_x ] target simulated area in x axial direction
[ --target_area_y ] target simulated area in y axial direction
[ --num_probe_x_tiles ] number of point grid in x axial direction
[ --num_probe_y_tiles ] number of point grid in y axial direction
[ --layout_min_x ] Reference coordinate in x axial direction
[ --layout_min_y ] Reference coordinate in y axial direction
[ --power_grid_map ] generate power grid map for GAN training and EM prediction
```
```
create_grid_map(4-pad).py
optional arguments:
[ --metal_layers ] target metal layers
[ --target_area_x ] target simulated area in x axial direction
[ --target_area_y ] target simulated area in y axial direction
[ --num_probe_x_tiles ] number of point grid in x axial direction
[ --num_probe_y_tiles ] number of point grid in y axial direction
[ --layout_min_x ] Reference coordinate in x axial direction
[ --layout_min_y ] Reference coordinate in y axial direction
```
There is only one power grid map of one layout design, which is used in both the model training and EM prediction phases.
- Note:
- You need to get the coordinates of VDD and VSS in the layout design.
- You should choose different scripts for 2-pad and 4-pad power supply designs.
### EM Map
```
create_em_map.py
optional arguments:
[ --EM_path ] path to the simulated EM data
[ --num_input_stimuli ] number of plaintexts
[ --num_probe_x_tiles ] number of point grid in x axial direction
[ --num_probe_y_tiles ] number of point grid in y axial direction
[ --time_steps ] simulation time for each EM trace
[ --em_map_train ] generate em map for GAN training
[ --em_map_test ] generate em current map for EM prediction
```
## GAN Model Training
We aim to design and train a GAN for EM prediction.
- Note:
- The generator G accepts cell current maps, power grid maps and time sequence.
- Both the EM maps predicted by G and the real EM maps, together with the input maps of G, are alternatively fed to the discriminator D for determination.
- The results of D are further fed back to G to enhance the quality of the predicted EM maps
```
GAN4EM_training.py
optional arguments:
[ --num_probe_x_tiles ] number of point grid in x axial direction
[ --num_probe_y_tiles ] number of point grid in Y axial direction
[ --time_steps ] simulation time for each EM trace
[ --percent_valid_split ] 10% of training points is used for validation during training
[ --learning_rate_val ] learning rate decays exponentially from 0.0005
[ --num_input_stimuli ] number of plaintexts
[ --decay_rate_val ] learning rate with the discount factor 0.98
[ --epochs ] number of times the model worked on the entire training dataset
[ --batch_size ] number of samples processed per training epoch
[ --trained_model_path ] save the trained weight parameters
[ --training_current_map ] input cell current map for model training
[ --training_gird_map ] input power grid map for model training
[ --training_em_map ] input EM map for model training
```
## EM Prediction
The generator G is preserved and serves as an inference model for EM prediction.
```
GAN4EM_prediction.py
optional arguments:
[ --num_probe_x_tiles ] number of point grid in x axial direction
[ --num_probe_y_tiles ] number of point grid in Y axial direction
[ --time_steps ] simulation time for each EM trace
[ --percent_valid_split ] 10% of training points is used for validation during training
[ --learning_rate_val ] learning rate decays exponentially from 0.0005
[ --num_input_stimuli ] number of plaintexts
[ --decay_rate_val ] learning rate with the discount factor 0.98
