innovative AI-DRIVEN DESIGN Technologies

Our silicon-proven machine learning models can explore vast design spaces, optimize transistor-level performance, and continuously refine circuit behavior. The result is silicon that performs beyond what traditional design approaches can achieve.  

Our machine learning algorithms can optimize analog designs in minutes and improve Performance, Power and Area by up to 30%.

 Based on silicon-proven highly accurate machine leaning models, our software-defined analog technology allows runtime re-configurability, enabling customers to adjust performance targets, operating modes, and features dynamically in the field with 100% confidence. This extends product lifetimes, reduces SKUs, and opens new possibilities. 

 Every IP block we deliver is designed to self-calibrate and adapt across process, voltage, temperature, and aging — without external monitors or bulky hardware overhead. Performance remains within 1-2% of nominal, even under extreme conditions.  

This breakthrough applicable to all technologies and to both integrated and discrete circuits, marking a significant leap in analog design.  

 We go beyond static specifications. Our AI-Datasheet provides predictive performance modeling across all conditions, enabling right-first-time integration and deep insight into system behavior before silicon arrives. 

  This is not incremental improvement — it’s a fundamental shift. Our technology transforms analog from a fixed, static function into a living, adaptive part of the system, capable of evolving alongside mission needs.