Update README.md

README.md

@@ -9,6 +9,13 @@ EMSim+是一款基于生成对抗网络（GAN）的版图级芯片电磁仿真
 <img src="./docs/EMSim+ vs. EMSim.jpg" width=395px>
                         
 
+# 环境配置
+
+- 操作系统：Linux or Windows
+
+- 依赖项：Python 3.8+ with PIP,  TensorFlow2.4 with GPU
+
+
 
 # Table of contents
 - [Prerequisites](#prerequisites)
@@ -21,15 +28,9 @@ EMSim+是一款基于生成对抗网络（GAN）的版图级芯片电磁仿真
 - [Contributors](#contributors)
 - [Copyright](#copyright)
 
-# Prerequisites
-At a minimum:
+# 实现框架
 
-- Python 3.8+ with PIP
-- TensorFlow2.4 with GPU
-- Linux or Windows
-
-# Running EMSim
-EMSim+ consists of three main steps: feature extraction, GAN model training and EM prediction.
+EMSim+的实现框架包含特征提取，GAN模型训练和电磁预测三部分。
 
 <table>
   <tr>
@@ -40,14 +41,14 @@ EMSim+ consists of three main steps: feature extraction, GAN model training and
 
 ## Current Analysis and Electromagnetic Computation
 
-These two steps are from EMSim and are intended to subsequently produce training data for GANs.
+这两部分是使用EMSim完成的，参考链接：
+由Current Analysis and Electromagnetic Computation获得的单元电流信息、电源位置信息和电磁计算结果。
 
 ## 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.
+特征提取的目的是从芯片物理布局数据库中提取单元电流、电源网格和电磁信息，并将其转换为特征图。
 
-### Cell Current Map
+### 单元电流地图
 
 ```
 create_current_map.py
@@ -68,7 +69,7 @@ optional arguments:
   
 ```
 
-### Power Grid Map
+### 电源网格地图
 
 ```
 create_grid_map(2-pad).py
@@ -95,14 +96,16 @@ optional arguments:
   [ --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.
+
+
+对于一个版图级设计，只有一个电源网格地图，在模型训练和电磁预测阶段均使用该电网图。
+- 注意
+    - 需要在版图级设计中获取 VDD 和 VSS 的坐标。
+    - 对于 2-pad 和 4-pad 的不同电源设计需要选择不同的脚本。
 
 
 
-### EM Map
+### 电磁地图
 
 ```
 create_em_map.py
@@ -121,11 +124,7 @@ optional arguments:
 ## 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
+GAN网络采用U-net结构搭建生成器，采用PatchGAN结构搭建判别器。
 
 
 ```
@@ -148,7 +147,7 @@ optional arguments:
 ```
 ## EM Prediction
 
-The generator G is preserved and serves as an inference model for EM prediction.
+在电磁预测阶段，训练好的生成器被保留下来，用做推理模型。
 
 ```
 GAN4EM_prediction.py