Presentation + Paper
4 April 2017 Identification and sensitivity analysis of a correlated ground rule system (design arc)
Eric Eastman, Dureseti Chidambarrao, Werner Rausch, Rasit O. Topaloglu, Dongbing Shao, Ravikumar Ramachandran, Matthew Angyal
Author Affiliations +
Abstract
We demonstrate a tool which can function as an interface between VLSI designers and process-technology engineers throughout the Design-Technology Co-optimization (DTCO) process. This tool uses a Monte Carlo algorithm on the output of lithography simulations to model the frequency of fail mechanisms on wafer. Fail mechanisms are defined according to process integration flow: by Boolean operations and measurements between original and derived shapes. Another feature of this design rule optimization methodology is the use of a Markov-Chain-based algorithm to perform a sensitivity analysis, the output of which may be used by process engineers to target key process-induced variabilities for improvement. This tool is used to analyze multiple Middle-Of-Line fail mechanisms in a 10nm inverter design and identify key process assumptions that will most strongly affect the yield of the structures. This tool and the underlying algorithm are also shown to be scalable to arbitrarily complex geometries in three dimensions. Such a characteristic which is becoming more important with the introduction of novel patterning technologies and more complex 3-D on-wafer structures.
Conference Presentation
© (2017) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Eric Eastman, Dureseti Chidambarrao, Werner Rausch, Rasit O. Topaloglu, Dongbing Shao, Ravikumar Ramachandran, and Matthew Angyal "Identification and sensitivity analysis of a correlated ground rule system (design arc)", Proc. SPIE 10148, Design-Process-Technology Co-optimization for Manufacturability XI, 101480I (4 April 2017); https://doi.org/10.1117/12.2258002
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KEYWORDS
Monte Carlo methods

Lithography

Manufacturing

Design for manufacturability

Tolerancing

Detection and tracking algorithms

Process engineering

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