proteanTecs provides a cloud-based platform that utilizes machine learning and on-chip monitoring technology to deliver real-time analytics on the health and performance of electronic components. This solution enables manufacturers to gain continuous visibility throughout the design, production, and operational phases, thereby enhancing reliability and reducing operational risks.
Funding
$190.5M 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
The increasing complexity of advanced electronics, driven by nano-scale production and advanced packaging, creates challenges in maintaining performance, power efficiency, quality, and reliability throughout the lifecycle. Traditional methods lack continuous visibility into chip health, leading to increased risks in design, production inefficiencies, and potential in-field failures. This lack of comprehensive monitoring impacts profitability, time-to-market, and the ability to scale electronics effectively.
Solution
proteanTecs offers a cloud-based platform that provides deep data analytics and continuous monitoring of electronic components, from design to in-field operation. By embedding on-chip agents (Monitoring IPs) and applying machine learning to the data they generate, the platform unlocks insights into chip performance, quality, and reliability at every stage of the value chain. This enables a common data language, providing continuous visibility, uncovering critical issues, and facilitating informed decision-making. The solution breaks the trade-off equation of performance, power, quality, and reliability, paving the way to new economics for scale.
Target Audience
The primary target audience includes semiconductor manufacturers, system integrators, and service providers in industries such as automotive, communications, and high-performance computing.
Features
- On-chip Agents (Monitoring IPs) embedded within the silicon die to collect real-time performance and health data.
- Cloud-based platform for data aggregation, processing, and visualization.
- Machine learning algorithms to analyze data and provide actionable insights and predictions.
- Comprehensive monitoring across the entire lifecycle, including chip design, production, system integration, and in-field operation.
- Real-time alerts and notifications for early detection of potential issues.
- Predictive analytics to anticipate failures and optimize maintenance schedules.
- Customizable dashboards and reports for visualizing key performance indicators (KPIs).
- APIs for integration with existing design, manufacturing, and operational systems.