Semiconductors

Improve ML prediction of material properties, chip design, maintenance, and more

Whether you’re predicting the properties of materials, designing new chips, or predicting component failure, Dark Matter delivers a fast, affordable way to boost the performance of your ML models in complex settings.

Dark Matter thrives in complex problem settings

Discovery and Development

Simulate manufacturing processes effectively without the need for extensive physical experimentations.

Precision Yields & Processes

Interpret data from various process stages to more accurately understand and and forecast yields.

Anticipate Equipment Failures

Reduce downtime by predicting equipment and sensor data to predict failures before they occur.

Retain control of your data and models

Slots in Seamlessly

Surprisingly lightweight, Dark Matter represents a transformative new step in the data science pipeline that doesn’t alter your existing processes.

Domain and Model Agnostic

We make any model in any domain better simply by creating richer representations of the relationships in your existing data.

Secure Integration

Integration is available on-premises or via cloud API. Retain total control of your pipeline, keeping the privacy and integrity of your data intact.

Backed by:

logo-Salesforce-Ventures-white
logo-motivate-venture-capital-white
logo-M13-white
logo-AMPLO-white

Ready for better model performance?

Get in touch to learn more and book a demo.

Frequently Asked Questions

Dark Matter is available for on-premises installation on your machine and using your compute resources, enabling you to retain complete control over your proprietary data. On-prem deployment ensures that we never see your data.

We encourage you to try Dark Matter with your data and model to compare the results with your existing pipeline. Most customers use it in a testing environment with sample data to minimize resource requirements before putting it into production. If you’d like to set up a trial, please fill out the form here and we will be in touch.

While Dark Matter does create new variables, its mechanics are fundamentally different. Traditional synthetic data recreates existing distributions from Gaussian noise, so no new information is created. This has the virtue of anonymizing data (which is essential for some regulated industries), but it has minimal impact on predictive accuracy as it mirrors the statistical properties of your data.

In contrast, Dark Matter learns how to create embeddings that have different statistical properties and distributions. Using our new machine learning algorithm, it’s able to converge on nearly orthogonal features that measurably improve predictive accuracy.

One of the primary benefits of Dark Matter is that it lowers the barrier to useful predictive performance by creating richer representations of your data. That said, there is a theoretical minimum threshold of data quality and volume that can be useful (i.e. if what you’re working with is mostly noise, it probably won’t help). Our rule of thumb is that if you have a working data science pipeline that’s generating mediocre predictions, Dark Matter can improve its performance. 

Research

Dive into our cutting-edge research.

Feature Enhancement: A New Approach for Representation Learning (Whitepaper)

Discover a novel approach to representing complex, non-linear relationships inherent in real-world data.

Feature Programming for Multivariate Time Series Prediction (ICML)

Learn about a new framework for automated feature engineering from noisy time series data.

Resources

Blog
Op-eds and thoughts on the state of machine learning and AI
Documentation
Developer support, API docs, quick-start guide
Published Research
Ensemble research, papers, and conferences

Join the Waitlist

Early Access Form