Summary ------------- IceLab provides designers of semiconductor components with Python tools that help them define and carry out highly-automated devices measurements and data analysis. It supports local and remote operation of semiconductor wafer probe-stations and measurement equipment. Data is stored in a MongoDB database for subsequent analysis, including interactive visualization with Bokeh or Matplotlib. Abstract ----------- Evaluation of new semiconductor device components is time-consuming and often involves a lot of manual work. Good examples of such evaluations are in the fields of parametric and reliability analysis. IceLab helps automate a significant part of this process, thereby saving time and increasing the number of data points for statistical analysis. This framework is already being used successfully within the semiconductor industry. With IceLab, designers can write dedicated measurement flows in Python. These can be executed directly as Python programs or invoked through a GUI that facilitates device selection, wafer stepping control, and data visualization. IceLab provides drivers to control probe-stations and measurement equipment over ethernet and GPIB networks. In fact, it has already been used to remotely control laboratory equipment across continents. Measured data can be stored in MongoDB databases for later processing. Data from multiple devices with varying geometrical dimensions, varying process conditions, or from different wafer locations can be combined to form meaningful interactive visualizations for analysis. Both Matplotlib and Bokeh are used for data visualization. This presentation will show how tools like MongoDB, Numpy, Pandas, Matplotlib, and Bokeh are integrated into a data-processing pipeline for semiconductor device analysis.