The startup offers a semiconductor design platform that utilizes machine learning for automated modeling, enabling self-learning from collected data. This technology reduces development time and minimizes design errors, enhancing the efficiency of semiconductor design processes.
Funding
$820K raised to dateRaised to date based on public sources. This may differ from the amount the company actually raised and is based only on what is publicly available on the internet.
Founders
Product
Problem
Developing accurate semiconductor device models and optimizing manufacturing processes are traditionally time-consuming and resource-intensive, often requiring years of manual effort and extensive experimental data. Existing modeling solutions may lack the accuracy needed for advanced circuit simulations, and process optimization can be hampered by limited data and an inability to account for complex physical phenomena.
Solution
Alsemy offers an AI-powered Electronic Design Automation (EDA) platform that accelerates semiconductor design and manufacturing. The platform leverages machine learning and physics-informed AI to automate device modeling, process optimization, and design-technology co-optimization (DTCO). Alsemy's solutions, including Alsis, Alsphere, and Alscope, enable rapid generation of accurate neural network-based compact models, AI-driven process simulations, and streamlined collaboration across the semiconductor ecosystem. By integrating AI into the design and manufacturing workflow, Alsemy reduces development time, minimizes design errors, and enhances the efficiency of semiconductor processes.
Target Audience
Alsemy's primary customers are semiconductor engineers, researchers, and manufacturers involved in device modeling, process optimization, and DTCO, as well as research institutes and SMEs.
Features
- **Alsis:** Generates neural network-based device compact models for accurate circuit simulations, reducing model development time from years to minutes.
- **Alsphere:** Provides an AI-driven platform for optimizing semiconductor manufacturing through process modeling simulations and reverse engineering, including 3D visualization and recipe recommendations.
- **Alscope:** Offers an AI-powered DTCO platform that streamlines semiconductor design, optimization, and analysis, integrating TCAD simulations, SPICE models, and experimental data.
- **Large Physics Model (LPM):** Employs physics-informed AI to ensure models adhere to semiconductor device physics, addressing limitations of purely data-driven ML models.
- **Automated Machine Learning (AutoML):** Provides an easy-to-use AutoML algorithm that automatically determines the optimal network structure based on customer data, even for non-AI experts.
- **Knowledge Transfer Algorithms:** Uses knowledge transfer algorithms to incorporate prior knowledge in the learning process, enabling effective modeling even with limited data.
- **Seamless Integration:** Integrates with popular commercial SPICE software and EDA tools to enhance existing workflows.