Failure Mechanisms · foundry-ml
electromigration_v1-1
Electromigration dataset of effective charge values for 49 metal host-impurity pairs in interconnect materials.
What electromigration_v1-1 is
electromigration_v1-1 is a tabular materials science dataset developed by Yu-chen Liu, Ben Afflerbach, Ryan Jacobs, Shih-kang Lin, and Dane Morgan. It compiles effective charge measurements across interconnect materials to investigate electromigration, an electrical transport process where momentum transfer from conducting electrons induces ion movement.
The dataset addresses the challenge of understanding and predicting structural failure rates in microelectronic devices caused by electromigration. By linking elemental host-impurity pairings directly to alloy effective charge values, it provides targeted data for training machine learning algorithms in materials science and engineering.
How it was made
The dataset consists of 49 metal host-impurity pairs provided in a tabular Parquet structure. Each entry records the host element and the impurity element as categorical inputs, alongside a continuous numerical target representing the alloy effective charge value for regression.
The data is structured as a single training split, prepared for use with tabular regression and classification workflows.
Key facts
- Publication Year
- 2022
- Publisher
- Materials Data Facility
- Dataset Size
- 49 metal host-impurity pairs
- Data Format
- Tabular (Parquet)
- Supported Tasks
- Tabular regression, tabular classification
- Input Features
- Host element, Impurity element
- Target Feature
- Effective_charge_regression
- DOI
- 10.18126/abxi-r7eb
What you can use it for
- Effective Charge Regression
- Train machine learning models on elemental host and impurity pairings to predict alloy effective charge values.
- Interconnect Reliability Assessment
- Estimate electromigration susceptibility and device failure rates in microelectronic interconnects using predicted charge parameters.
- Tabular Classification of Alloy Systems
- Categorise metal host-impurity systems based on effective charge characteristics.
Explore the data
The schema, real rows, full-text search and a SQL filter — read in your browser, nothing downloaded.
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Strengths and limits
Good at
- Provides curated alloy effective charge values specifically targeted at microelectronic interconnect failure mechanisms.
- Delivered in standard tabular and Parquet formats compatible with major data science libraries.
- Features a clear, minimal schema focused directly on metal host and impurity relationships.
Watch out for
- Sample size is restricted to 49 metal host-impurity pairs.
- Contains only a single training split with no pre-defined validation or test sets.
- Input features are limited to elemental identifiers without supplementary experimental or environmental conditions.
Questions
Can I look inside electromigration_v1-1 without downloading it?
Yes. This page reads its schema, real rows and per-column statistics in the browser, with full-text search and a SQL filter. Nothing is downloaded to your machine.
What licence is electromigration_v1-1 under?
Its authors declare cc-by-4.0. Read the terms on the original page before reusing the data.
What variables are included in the dataset?
The input variables are the host element and impurity element, and the target variable is the alloy effective charge regression value.
How large is the dataset?
The dataset contains 49 metal host-impurity pairs.
What primary application does this dataset serve?
It supports machine learning prediction of effective charge to evaluate failure rates and electromigration in microelectronic interconnects.
How to cite it
Liu, Yu-chen, Afflerbach, Ben, Jacobs, Ryan, Lin, Shih-kang, and Morgan, Dane. (2022). Exploring effective charge in electromigration using machine learning. Materials Data Facility. https://doi.org/10.18126/abxi-r7eb
Written from electromigration_v1-1's own dataset card. Statements citing a figure that does not appear there are removed before publication. Licence and running details come from our registry.