基于数字孪生的芯片系统级测试装备快速定制设计方法研究OACHSSCDCSTPCD
A Rapid Customization Design Method of System-Level Test Equipment for Chips Based on Digital Twins
为了解决芯片系统级测试(system level test,SLT)装备定制化程度高,设计知识无法有效复用,从而导致的设计周期长的问题,对SLT装备的快速定制设计方法进行研究.构建SLT装备的设计知识原型,提出一种装备快速设计框架,包括基于核心设计参数的设计知识表征和机械模块间特殊的聚合方式,即"靠接"行为,有效地实现了装备设计知识的存储和复用.基于构建的设计知识原型,结合数字孪生技术,利用团队开发的数字工厂仿真平台,封装SLT装备组件库,搭建SLT装备数字孪生设计原型,以参数配置的方式快速输出三维设计方案.采用半实物在环仿真技术进行虚拟调试,使装备的设计制造与调试并行并互相印证.对于装备的设计以及调试,相比于传统方法,周期降低约30%,成本降低约40%,人力投入降低约60%.
In order to address the issues of long design cycles for system-level test(SLT)equipment caused by high customization requirements and ineffective reuse of design knowledge,a rapid customization design method for SLT equipment is studied.First,the design knowledge prototype of SLT equipment is developed.Additionally,a framework for rapid design of equipment is proposed,including the design knowledge characterization based on core design parameters and a special aggregation method between mechanical modules,i.e.,"leaning"behavior,which effectively realizes the storage and reuse of design knowledge for such equipment.Then,based on the developed design knowledge prototype,combined with digital twin technology,we use the digital factory simulation platform developed by our team to encapsulate the SLT equipment component library and build the SLT equipment digital twin design prototype.In this way,the 3D design solutions by parameter configuration can be quickly output.Ultimately,the semi-physical in-the-loop simulation technology is adopted for virtual debugging,making designing,manufacturing,and debugging of the equipment to proceed in parallel and verify each other.Compared with traditional methods,the approach proposed in this paper reduces the cycle time by about 30%,the cost by about 40%,and the labor input by about 60%for designing and debugging of such equipment.
林大钦;赵荣丽;赖苑鹏;刘强
广东工业大学 省部共建精密电子制造技术与装备国家重点实验室,广东 广州 510006
经济学
SLT装备数字孪生快速设计设计知识表征
SLT equipmentdigital twinrapid designdesign knowledge representation
《工业工程》 2024 (003)
22-30 / 9
广州市科技计划资助项目(2024A04J6301)
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