Wafer Heatmap Visualization

Heatmap sometime could be extremely useful, you can gain intuitive insight from the plot which contains information on both location and distribution, and manage to find potential pattern behind it.

This package is implemented on the top of seaborn, and further customized by matplotlib. It provides an easy way to visualize wafer map data, both numerical and categorical data are supported.

Before you start, you should have some knowledge on pandas and transform your data into pd.DataFrame, then make sure that positional data (x/row,y/col) are included in separate columns and encoded as integer.

Installation

To install wfmap via PyPI using pip:

pip install wfmap

or build the latest release from Github:

git clone https://github.com/xlhaw/wfmap.git
cd wfmap
python setup.py install

Basic Usage

For demonstration, I generate some dummy data under the /data folder. Let's load the data at first and explore the usage of this package.

import pandas as pd
from wfmap import wafermap
data=pd.read_csv('/data/demo.csv')

To better understand the data, take the first entry for example, it suggests that the Die#0 which located at #11 row and #60 col in wafer map, is OK defined by Defect Code and its Metrics is 84.3.

Numerical Data

'MAP_ROW' and 'MAP_COL' are the default column name for wafer mapping. If you have preprocessed your data as the same format as I did above. The command required could be as simple as follows:

wafermap(data,value='DATA',dtype='num')

On the left side of heatmap is the horizontal histogram plot of DATA, with colorized y-axis and invisible x-axis for visual aesthetics.

**Categorical Data **

Similar to above numerical/continuous data, categorical data such as Defect Code CODE can also be visualized as below.

wafermap(data,value='CODE',dtype='cat')

In addition to the regular heatmap, I put the histogram subplot and pie chart inset on the right half. For the sake of simplicity, only the ratio of top 5 categories will be annotated.