Sustainability Module

Data for Pioneers

Athinia's industry-wide data ecosystem helps drive business decisions based on big data and semiconductor process knowledge

SCC Scope 3 Working Group Members Highlight
Many Common Challenges and Opportunities

The Solution

Athinia unlocks a new standard for sustainability
management in the semiconductor industry

Key Attributes of the Athinia Sustainability Module

Semiconductor Specific Solution

• Follow standards adopted by SCC members, including the GHG Protocol
• Access industry certified data sources

Integrated Data Collaboration Platform

• Athinia platform adopted by leading semi-players with broader ecosystem building
• Manage and analyze data in a private space
• Share data and methodologies with selected upstream and downstream partners

Security Approved by Semi-Partners

• Data governance
• Infrastructure security
• Data protection
• Ecosystem rules of engagement in place
• Data lifecycle management

Data Architecture

• ML and Advanced Analytics
• Learning cycles and Data models and knowledge objects with data flow from suppliers to fabs

Supply Chain View

Bridging the gaps between process engineering and data science

Growing list of customers have clear needs for established (no code) analysis workflows available on the platform

• Micron: "not all suppliers have data scientists like
  EMD" & "engineers need to be able to deep dive into the data with coding"
• Intel: "it's a good idea to invest and develop some
  templates to help Subject Matter Excerpts to quick meaningful statistics / models synonymously implemented in JMP without having to get into code"
• DuPont: "can we repeat what we already do faster
  and easier"
• FujiFilm EM: "we don't have these capabilities"
• Kokusai: "will be challenging to adopt without
  availability  of widgets to enable process engineers with no Python/coding skills to jump into and use"

Generating insights
in no-code environment

A wide array of composable templates for self-service, no-code data analytics

Data preparation: Joins and aggregations

Testing of Assumptions: Suggestion of best methodology for shape of data

Feature reduction:

• KernelPCA
• PCA
• PLS

• Correlation
• TSNE
• Lasso Regression

Descriptive/Predictive models:

• OLS Linear Regression
• Logistic Regression

• XGBoost Regressor
• Random Forest

Visualizations:

• Box Plots
• Scatter Plots
• PCA Feature Importance

• SHAP Feature Importance
• Correlation Matrix

Recommendation engine suggests model and surfaces data issues

• Customer datasets are tested against hundreds of assumptions, curated and maintained by our data science team
• Tool clearly identifies limitations in the source data and suggests improvements
• Suitable algorithms are highlighted and can be deployed with a few clicks
• Assumptions are tested continuously as the underlying data updates

Athinia uses Palantir HyperAuto (Software-Defined Data Integration), a suite of capabilities designed to provide end-to-end data integration capabilities out of the box on top of the most common and mission-critical systems at organizations. It empowers you to autonomously create valuable workflows with ERP, CRM, and other organization-critical data. You can sync, integrate, and structure data to immediately build new workflows on top of major data systems in a matter of minutes.

Systems supported are:

• SAP
• Salesforce
• Oracle NetSuite
• Hubspot

Overview

HyperAuto consists of three components designed to integrate data from raw sources to ontology with minimal effort:

• Connectors enable transfer of large-scale data in a secure and optimized way from and to source systems.
• Source exploration allows rapid data discovery in a guided manner, and a "shopping cart" experience for rapid bulk data sync creation and configuration.
• Automatic pipeline generation transforms raw data into curated Foundry datasets and object types in the Ontology using automatically generated data pipelines.