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DC25_SLIDES_Track3_DevelopmentOfACo-PackagedApplication_XU_V2.pdf

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1、Information Classification:GeneralWelcome to ConferenceJanuary 2830,2025Santa Clara Convention Center1ExpoJanuary 2930,2025 Information Classification:GeneralDevelopment of a co-packaged Application Specific IC with direct drive optical engine chipletsQing Xu(Ranovus)Jeff Hutchins,Bin Cao,Omid Jafar

2、i,Georg Roell,Hojjat Salemi,Hamid Arabzadeh(Ranovus Inc.)Chih-Yi Kuan,Yu-Cheng Hsu,Chih-Wei Sung(MediaTek Inc.)Bono Liao,Brad Hsu,Mark Lin(Foxconn Interconnect Technology)2Information Classification:GeneralImageSPEAKERSQing XuSr.Technical Leader of Application Engineering,Ranovus I|Qing Xu holds a B

3、SEE degree from Shanghai Jiao Tong University,a Master Ingnieur degree from ESME Paris,and a Ph.D.in Electrical Engineering from Tlcom Paris(ENST).He joined RANOVUS Inc.in 2017,where he currently serves as Senior Technical Leader of Application Engineering.Qing has also held various roles at Belden

4、and Ericsson.He is the author of over 30 conference and technical papers and holds six granted U.S.patents.His current research focuses on monolithic integrated photonics transceivers,with an emphasis on close integration and co-optimization with high-speed SerDes.3Information Classification:General

5、Development of a co-packaged Application Specific IC with direct drive optical engine chipletsIntroduction to Co-Packaged Optical Engine ChipletsKey Components and CPO System DesignSystem Co-SimulationCPO System Integration and Bring-UpTest Results of the CPO SystemRoadmap for Scaling to 200G Signal

6、ingAgenda4Information Classification:GeneralAI training utilizes large quantities of matrix multiplicationGPUs are designed to accelerate“multiply and add”operations used in AI matrix multiplicationEach row in matrix A is paired with every column in matrix B Lots of computation with lots of paramete

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1. **项目目标**:开发ASIC与8×800G光引擎芯片片(OE)的共封装(CPO)系统,演示异构集成技术,证明可靠性、良率及供应链。 2. **核心组件**:采用Ranovus Odin®8 OE芯片,支持106.25Gbps PAM4信号,集成或外置激光源,通过0.4mm LGA插座封装。 3. **性能数据**:系统BER达2E-10至2E-8,OE功耗32W(4W/通道),较可插拔模块功耗降低约50%。 4. **热设计**:热仿真确保OE温度<70°C(ASIC 150W,环境45°C),采用精密压缩机制与散热盖。 5. **路线图**:目标扩展至200G信号,通过非重定时OE优化延迟与能效,支持AI集群高密度互联。
CPO如何降本增效? 光引擎芯片有何优势? 200G如何实现?
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