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Ratings and Reviews 0 Ratings
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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 Schrödinger?
Transform the domains of drug development and materials science by employing advanced molecular modeling approaches. Our computational platform, rooted in the principles of physics, offers distinct solutions for predictive modeling, data analysis, and collaborative efforts, enabling efficient exploration of chemical space. This state-of-the-art platform is utilized by top industries worldwide, supporting drug discovery projects and materials science endeavors in diverse fields such as aerospace, energy, semiconductors, and electronic displays. It propels our internal drug discovery initiatives, managing the entire process from identifying targets to discovering hits and optimizing leads. Moreover, it boosts our collaborative research aimed at developing innovative medicines to tackle major public health issues. With a dedicated team comprising over 150 Ph.D. scientists, we invest considerable resources into research and development. Our impact on the scientific community is highlighted by over 400 peer-reviewed publications that demonstrate the effectiveness of our physics-based approaches, ensuring we remain leaders in the evolution of computational modeling techniques. We are unwavering in our commitment to pioneering advancements and broadening the horizons of our industry while fostering partnerships that amplify our research capabilities.
Integrations Supported
Additional information not provided
Integrations Supported
Additional information not provided
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
Not specified
Training Options
Documentation Hub
Training Options
Not specified
Company Facts
Organization Name
Quantum Materials
Company Location
South Korea
Company Website
semiprocesslab.com
Company Facts
Organization Name
Schrödinger
Company Location
United States
Company Website
www.schrodinger.com
Categories and Features
Materials Science
Not specified
Categories and Features
Drug Discovery
Not specified
Materials Science
Not specified
Molecular Modeling
Not specified
Simulation
Not specified