SandBox AnalyticsTM

Quickly identify target process windows
based on built-in models and
experimental results

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Experimentally Validated

SandBox AnalyticsTM allows engineers to make data-driven decisions when performing experimental design. 3X ↓ in process development cost without compromising process quality and 3X ↑ time to market

Perform DOEs

The SandBox Oculus’s sharing and collaboration tools allows engineers to securely store, organize, and share all their data-in real-time. The Semiconductor fabrication process require the optimization of tens of process parameters. Tweaking one parameter at a time is impractical and inefficient. In SandBox Analytics, we offer a complete library of classical DOE designs for our customers.

We also offer our own proprietary capabilities for model-driven experimental design where we link with SandBox models to help your engineers pick the experiments that will reduce the uncertainties surrounding the process space they are working.

Predict Process Outcomes

At SandBox, we recognize the complexity of the physical processes our customers are dealing with. In our industry, process development is a moving target, so we obsess over having a malleable simulation framework. Our bread and butter is in building out fast, flexible, and accurate models depending on our customers need.

Physical
models

Statistics
    and
Machine
Learning

Physical Models

We have a breadth of expertise in process modeling of critical fabrication steps including plasma etching and deposition and atomic layer etching and deposition. Our databases of models ranges from continuum models to molecular dynamic models to reactor scale models.

Statistics and Machine Learning Models

We offer statistics and machine learning models for customers who want to make predictions based on solely on their experimental data. These models include linear and nonlinear regressions, neural networks, Naïve Bayes and SVMs.

Hybrid Models

In semiconductor fabrication, there are often too many unknowns about a given process for customers to build out a physical model. At the same time, they are often working with very limited experimental data sets making statistics and machine learning models unviable. In this information gap, SandBox specializes in what we call hybrid models. We use physics to build rule-based models to constrain the system we are working with and machine learning models to construct predictions. With our in-house technologies, we are then able to create a high accuracy predictions of process spaces using minimal inputs from our customers.

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