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Logic Yield Learning Vehicle Failure Analysis in Technology Development

机译:技术开发中的逻辑良率学习型车辆故障分析

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With the microelectronic technology progresses in nanometer realm, like SRAM, logic circuits and structures are also becoming dense and more sensitive to process variation. Logic failures may have different root causes from SRAM failure. If these technology weak points for logic circuits are not detected and resolved during the technology development stage, they will greatly affect the product manufacturing yield ramp, leading to longer time of design to market. In this paper, we present a logic yield learning methodology based on an inline logic vehicle, which includes several scan chains of different latch types representative of product logic. Failure analysis for the low yield wafers had revealed several killer defects associated with logic circuits. A few examples of the systematic failures unique to logic circuits will be presented. In combination with SRAM yield learning, logic yield learning makes the technology development more robust thus improving manufacturability.
机译:随着微电子技术在纳米领域的发展,例如SRAM,逻辑电路和结构也变得越来越密集,并且对工艺变化更加敏感。逻辑故障可能与SRAM故障有不同的根本原因。如果在技术开发阶段未发现并解决逻辑电路的这些技术弱点,则它们将极大地影响产品制造良率的提高,从而导致更长的设计上市时间。在本文中,我们提出了一种基于嵌入式逻辑工具的逻辑成品率学习方法,其中包括几个代表产品逻辑的不同锁存器类型的扫描链。对低成品率晶圆的故障分析显示出与逻辑电路相关的一些致命缺陷。将给出逻辑电路特有的系统故障的一些示例。与SRAM良率学习结合使用时,逻辑良率学习使技术开发更加稳健,从而提高了可制造性。

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