感存算三维堆叠封装设计数据集
收藏资源简介:
本数据集面向感存算三维堆叠封装的协同设计与验证,围绕 DFT—版图/互连—电源/信号完整性—热-力耦合 四条主线给出可追溯、可复现的多源证据。内容包括:跨裸片可测性结构(JTAG/IJTAG 网络、扫描链与 ATPG 向量及覆盖率报表),有源转接板与堆叠 RDL 的设计与 GDS 及物理验证产物(DRC/LVS/寄生抽取与签核),Cadence Sigrity 产生的 PI/SI 结果(IR-Drop、串扰、插损等),以及三维 FEA 平台得到的热-力仿真(结温分布、瞬态热响应、层间应力与翘曲曲线/云图)。数据同时提供功耗分布版图、几何模型、材料参数与边界条件表,并配套批处理脚本与出图模板,形成“模型—参数—结果—报告”的一一对应关系,支持初始方案与改进方案的定量对比。数据可直接服务于跨裸片 DFT 策略验证、RDL/互连与 PI/SI 联动优化、热-力边界条件向版图/封装的约束回推,以及面向量产的可测性与可靠性评审;数据开放共享,并符合工程复现实验的需求。
This dataset is targeted at the collaborative design and verification of 3D stacked packaging for memory-computation integration. It provides traceable and reproducible multi-source evidence along four core threads: Design for Testability (DFT), layout/interconnects, Power/Signal Integrity (PI/SI), and thermo-mechanical coupling. The dataset contents are as follows: 1. Inter-die testability structures: JTAG/IJTAG networks, scan chains, ATPG vectors and their coverage reports; 2. Designs of active interposers and stacked RDL, together with their GDSII files and physical verification deliverables including DRC reports, LVS checks, parasitic extraction results and sign-off documents; 3. PI/SI results generated by Cadence Sigrity, such as IR-Drop, crosstalk, insertion loss and other relevant data; 4. Thermo-mechanical simulations conducted via a 3D FEA platform, covering junction temperature distribution, transient thermal response, inter-layer stress and warpage curves/contour plots. In addition, the dataset provides power distribution layouts, geometric models, material parameter and boundary condition tables, as well as batch processing scripts and plotting templates. It establishes a one-to-one correspondence between "models, parameters, results and reports", supporting quantitative comparison between initial and improved design schemes. The dataset can directly serve the following applications: inter-die DFT strategy verification, co-optimization of RDL/interconnects and PI/SI, backward derivation of thermo-mechanical boundary constraints into layouts and packaging designs, as well as mass production-oriented testability and reliability reviews. The dataset is openly shared and fully meets the requirements of engineering reproducible experiments.




