Presentation
AI-Driven Multi-Parameters Multi-Objectives Optimization Flow For High-Speed Transmission Line In SerDes Design
DescriptionIn high-speed SerDes design, as data rates and line lengths increase, treating transmission lines merely as short interconnects in circuit simulation can lead to abnormal waveforms. To accurately address signal reflection, crosstalk, and attenuation, it becomes essential to adopt a precise transmission line model in high-speed channels simulation analysis. With process nodes shrinking and design complexity escalating, transmission line parameters, such as impedance, become increasingly sensitive to variations in width, spacing, shielding, etc. Additionally, the demand for space-sharing with other lines imposes stricter layout requirements. Constrained space makes routing more challenging, necessitating meticulous calculations and significantly increasing the workload for layout engineers.
In traditional design methods, layout engineers draw the transmission lines manually and flatly. As the design is not parameterized, any manual adjustment triggers a complete rework of the process, leading to low iteration efficiency. Furthermore, the relationship between transmission line design parameters and performance objectives is often unclear. When simulation results deviate from expectations, traditional design methods are difficult to pinpoint the root cause efficiently, making it difficult to identify effective optimization direction.
Here, we adopt AI-driven ML based MOP methods for high-speed transmission line design, enabling automatic multi-parameters, multi-objectives optimization. With this approach, we can efficiently obtain multiple designs that meet target values and dramatically reduce the layout iterations ranging from weeks to hours. Through this flow improvement, we make high-speed transmission line layout optimization easy and efficient, significantly improve design efficiency.
In traditional design methods, layout engineers draw the transmission lines manually and flatly. As the design is not parameterized, any manual adjustment triggers a complete rework of the process, leading to low iteration efficiency. Furthermore, the relationship between transmission line design parameters and performance objectives is often unclear. When simulation results deviate from expectations, traditional design methods are difficult to pinpoint the root cause efficiently, making it difficult to identify effective optimization direction.
Here, we adopt AI-driven ML based MOP methods for high-speed transmission line design, enabling automatic multi-parameters, multi-objectives optimization. With this approach, we can efficiently obtain multiple designs that meet target values and dramatically reduce the layout iterations ranging from weeks to hours. Through this flow improvement, we make high-speed transmission line layout optimization easy and efficient, significantly improve design efficiency.
Event Type
Engineering Presentation
TimeMonday, June 2311:15am - 11:30am PDT
Location2010, Level 2
AI
IP