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What is Semi Process Lab?

The Semi Process Lab is an innovative online platform specifically designed for professionals in the semiconductor and display fields, allowing them to accurately forecast process results. Its extensive array of Design · Predict tools incorporates numerous modeling methods for techniques like ALD, CVD, PECVD, PVD, RIE, and ALE, which are intended to help estimate vital metrics such as film growth, thickness, wafer uniformity, step coverage, and etch profiles. Users can delve into wafer map analyses and visualize intricate structures in 3D, including trenches, vias, or multi-layered designs, while also examining various equipment setups and device configurations. The platform promotes experimentation by offering model recommendations that help refine process parameters or create new experimental methodologies. In addition, it seamlessly integrates measured equipment data for calibration and provides a selection of design templates along with AI-enhanced image generation capabilities. Users have the convenience of exporting their results in various formats like PPTX, Word, Excel, and PNG, and they can also utilize free calculators for assessing film uniformity, ALD growth per cycle, etch selectivity, and step coverage. Ultimately, this web application not only delivers prediction outcomes as process-design estimates but also encourages users to validate these predictions on their specific equipment for accuracy. By fostering a comprehensive approach to design and analysis, the platform significantly enhances the productivity and efficiency of engineers in their optimization efforts. This robust functionality allows professionals to make informed decisions and drive innovation in their respective fields.

What is Microsoft Discovery?

Microsoft Discovery is a groundbreaking platform designed to transform the research and development process by embedding AI at every stage of the scientific method. By collaborating with specialized AI agents and leveraging a powerful graph-based knowledge engine, researchers can conduct experiments, generate hypotheses, and synthesize results more efficiently than ever before. The platform’s ability to reason over nuanced scientific data and provide transparent, context-rich insights fosters an environment where innovation can flourish. Designed for flexibility, Microsoft Discovery allows researchers to integrate their own models, tools, and datasets with Microsoft’s latest innovations, ensuring the platform can adapt to any research need. Built on the trusted Azure infrastructure, Discovery ensures full compliance, governance, and security, making it ideal for enterprise use. Early successes, such as the rapid discovery of a non-PFAS coolant prototype, showcase the platform’s ability to dramatically accelerate scientific research, delivering groundbreaking results that would have taken years to achieve using traditional methods. With a growing ecosystem of customers and partners across industries like pharma, energy, and materials science, Microsoft Discovery is poised to become a key tool for driving innovation across various scientific domains.

Media

Media

Integrations Supported

Integrations Supported

Azure HPC
Microsoft Copilot
Microsoft Foundry

API Availability

API Availability

Pricing Information

Plans use credits. New accounts receive free signup credits; additional credits are available through subscriptions or credit packs.

Pricing Information

Pricing not provided

Supported Platforms

SaaS

Supported Platforms

SaaS

Customer Service / Support

Web-Based Support

Customer Service / Support

Web-Based Support

Training Options

Documentation Hub

Training Options

Documentation Hub

Company Facts

Organization Name

Quantum Materials

Company Location

South Korea

Company Website

semiprocesslab.com

Company Facts

Organization Name

Microsoft

Date Founded

1975

Company Location

United States

Company Website

azure.microsoft.com

Categories and Features

Materials Science

Not specified

Categories and Features

Agentic AI

Not specified

AI Agents

Not specified

AI Science

Not specified

Materials Science

Not specified

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