DDR ROUTING GUIDELINES

  • DDR does require cleaner reference signals, reduced setup and hold times, and matched signal lengths to reduce signal skew effects.
  • Vref should be isolated from other nets, decoupled from both VDD (supply voltage) and VSS with balanced decoupling capacitors and routed at least 20 mm away from other signals.
  • Vref must track 0.5* VDD
  • Additionally, the use of a distributed decoupling scheme helps minimize capacitor ESL and localize transient currents and returns.
  • Route DDR signals only on layers that are directly adjacent to a common reference plane.
  • Keep the data (DQ), data strobe (DQS) and data mask (DM) signals related to the same byte lane matched in length to maximize the timing margins. Route each 8-bit data group (DQ, DQS, and DM) on the same layer with matched trace lengths (+/- 2.5 mm) to minimize skew. Vias and BGA breakout patterns should be included in the analysis of each trace length.
  • To help reduce crosstalk, isolate data and data strobes from the address and command signals by routing different signal types on separate layers of the PCB.
  • Separate data and control nets by a minimum of 0.5 mm to minimize crosstalk.
    Isolate signal groups using different resistor packs. Data signals and data strobes can be grouped together. Address and command signals can be grouped together, but use a different register pack from the one used for the data signals. Clock signals should also use a different resistor pack.
  • If DIMMs are used, place series resistors close to the first DIMM to simplify routing.
  • Place termination resistors on a top layer VTT island that is at the end of the bus.
  • Use wide island traces to increase current capacity.
  • Place the VTT generator as close as possible to the termination resistors.
  • Route data, address, and command signals in a daisy-chain topology.
  • Route control and clock signals point-to-point.
  • Do not allow total trace lengths for any point-to-point signal to exceed 50 mm.
  • Do not allow total trace lengths for any daisy-chained signal to exceed 75 mm.
(Kriging_NSGA2)克里金模型结合多目标遗传算法求最优因变量及对应的最佳自变量组合研究(Matlab代码实现)内容概要:本文介绍了克里金模型(Kriging)与多目标遗传算法NSGA-II相结合的方法,用于求解最优因变量及其对应的最佳自变量组合,并提供了完整的Matlab代码实现。该方法首先利用克里金模型构建高精度的代理模型,逼近复杂的非线性系统响应,减少计算成本;随后结合NSGA-II算法进行多目标优化,搜索帕累托前沿解集,从而获得多个最优折衷方案。文中详细阐述了代理模型构建、算法集成流程及参数设置,适用于工程设计、参数反演等复杂优化问题。此外,文档还展示了该方法在SCI一区论文中的复现应用,体现了其科学性与实用性。; 适合人群:具备一定Matlab编程基础,熟悉优化算法和数值建模的研究生、科研人员及工程技术人员,尤其适合从事仿真优化、实验设计、代理模型研究的相关领域工作者。; 使用场景及目标:①解决高计算成本的多目标优化问题,通过代理模型降低仿真次数;②在无法解析求导或函数高度非线性的情况下寻找最优变量组合;③复现SCI高水平论文中的优化方法,提升科研可信度与效率;④应用于工程设计、能源系统调度、智能制造等需参数优化的实际场景。; 阅读建议:建议读者结合提供的Matlab代码逐段理解算法实现过程,重点关注克里金模型的构建步骤与NSGA-II的集成方式,建议自行调整测试函数或实际案例验证算法性能,并配合YALMIP等工具包扩展优化求解能力。
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