A thermal simulation methodology is developed for interconnects enabled by a data-driven learning algorithm accounting for variations of material properties, heat sources and boundary conditions (BCs). The methodology is based on the concepts of model order reduction and domain decomposition to construct a multi-block approach. A generic block model is built to represent a group of interconnect blocks that are used to wire standard cells in the integrated circuits (ICs). The blocks in this group possess identical geometry with various metal/via routings. The data-driven model reduction method is thus applied to learn material property variations induced by different metal/via routings in the blocks, in addition to the variations of heat sources and BCs. The approach is investigated in two very different settings. It is first applied to thermal simulation of a single interconnect block with similar BCs to those in the training of the generic block. It is then implemented in multi-block thermal simulation of a FinFET IC, where the interconnect structure is partitioned into several blocks each modeled by the generic block model. Accuracy of the generic block model is examined in terms of the metal/via routings, BCs and thermal discontinuities at the block interfaces.
翻译:通过数据驱动学习算法,计算物质特性、热源和边界条件的变化,为相互连接开发了热模拟方法。该方法基于模型订单减少和域分解概念的概念,以构建多块办法。通用区块模型是用来代表一组用于在集成电路中连接标准单元格的互连区块的通用区块。该组块与各种金属/航线具有相同的几何测量法。因此,数据驱动模型减少法用于学习各区块中不同金属/航线造成的物质性质变化,以及热源和分解的变异。该方法在两个非常不同的环境中进行调查。该方法首先用于热模拟一个与类似碱区块的单个互连区块与通用区块培训中的单个互连区块。随后,该区块在FinFET IC的多块热模拟中实施,每个区块的互连结构都以通用区块模型隔成若干块块块。通用区块模型的准确性在金属/航线界面、BC和隔热层中进行检查。