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DCON26_PAPER_Track06_LinkDynamicModelingSimulationUsingIBIS-AMIFramework_69_28.pdf

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1、 Link Dynamic Modeling and Simulation using IBIS-AMI Framework Chih-Hsun Chou,AMD chih- Zach Myers,AMD Geoff Zhang,AMD Abstract In high-speed serial link applications,IBIS-AMI model is widely used for assessing link margin.However,todays industry specification and the associated EDA tools only accom

2、modate static modeling and consequent simulation.Static solutions incorporate only fixed channel models,a single TX equalization setting,and some other given settings,ignoring that real systems could change due to temperature,humidity,and so on.For instance,temperature change will change both silico

3、n behavior and channel loss profiles,leading to some optimal settings under one condition becoming sub-optimal or even detrimental.In this paper,we propose a dynamic modeling methodology,still using the IBIS-AMI framework,to close the gap between simulation and reality.Our approach allows dynamicall

4、y changing channel characteristics as well as TX parameters during time-domain simulations.We will show how a time varying channel and time varying TX FIR coefficients impact the adaptation and link performance of a serial link.We further show how dynamically changing the TX FIR coefficients compens

5、ates for the time-varying channel.Using the proposed dynamic modeling methodology,SerDes designers and system architects can improve the global link margin under specific application conditions.Authors Biography Chih-Hsun Chou received the Ph.D.degree in electrical engineering from the Department of

6、 Electrical and Computer Engineering,University of California at Riverside.He is currently working in AMD.His previous research projects include system level design and architecture with a specific focus on PCIe acceleration platform for SmartNIC.His current research interests include SerDes link pe

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1. **动态建模必要性**:传统IBIS-AMI静态仿真无法模拟温度、湿度等环境变化对高速串行链路(如212.5Gbps PAM4)的影响,导致评估过于悲观或乐观。 2. **动态仿真方法**:提出基于IBIS-AMI框架的动态建模(MP4AMI平台),支持时变信道(如温度梯度FIR系数)和TX参数(如FIR系数动态更新),确保状态连续性。 3. **关键发现**: - 温度变化案例:动态仿真BER低于静态仿真(如100°C时动态BER=3e-6 vs. 静态悲观预测),因RX自适应轨迹依赖历史状态。 - TX FIR更新案例:动态仿真收敛性优于静态(如[20,0]起始点下动态收敛,静态失败),减少30%仿真时间。 4. **应用价值**:动态仿真可优化链路裕度,避免过设计,未来结合机器学习(如决策树)提升环境鲁棒性。
**动态仿真优势?** **温度影响链路?** **TX FIR如何优化?**
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