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Overview

Athinia Catalyst provides powerful tools to explore, visualize, and analyze your semiconductor manufacturing data. Designed for engineers and analysts, these tools help you uncover hidden patterns, identify correlations, and extract valuable insights without requiring advanced programming or data science skills.

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Access Athinia Catalyst at catalyst.athinia.io.

Getting Started: Learn how to create your first study and upload datasets

Key Features​

Data Exploration​

  • Data Health: Automatically detect and analyze data quality issues including missing values, duplicate IDs, and cardinality problems
  • Statistics: Get a comprehensive view of your dataset's characteristics, including distributions, outliers, and data quality metrics
  • Univariate Outlier Detection: Identify data points that fall significantly outside normal distributions using robust statistical methods (IQR, MAD, Z-Score)
  • Filtering: Refine your dataset by creating precise filter conditions to focus on specific subsets of data

Data Visualization​

  • Plots: Create insightful visualizations using an intuitive drag-and-drop interface
  • Correlations: Discover relationships between variables using correlation matrices and ranked correlation views
  • Principal Component Analysis: Reduce high-dimensional data into a smaller set of uncorrelated components to uncover hidden patterns and facilitate clustering

Machine Learning & Analysis​

Data Management​

  • Data Types: Understand how different types of semiconductor data are classified and processed

Use Cases​

  • Process Optimization: Identify which parameters most strongly influence your yield or quality metrics
  • Root Cause Analysis: Uncover correlations between equipment settings and defect patterns
  • Quality Control: Build models to predict potential quality issues before they occur
  • Process Monitoring: Track critical parameters and identify trends or anomalies