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• W4367307939 abstract "Integrated circuit performance has been limited by transistor performance for many process nodes. However, in advanced nodes where pitches reach 10s of nanometers in size, there is an increasing probability of cases where circuit timing may be limited by the resistance and capacitance of the device rather than the transistor. This means that metal layer patterning may have implications on device performance beyond reliability, shorts, and opens. Lithography variation can be effectively predicted using stochastic simulations, including layer overlay. Simulating many patterns stochastically produces insight into the performance of the lithography process over time. Etching and metallizing the pattern set in simulation then allows the study to extend to electrical simulations. The combined lithography and electrical simulation data can then be used together to improve process or pattern performance before constructing a reticle. These data also allow the engineering teams to address resist and capacitance issues that may impact device performance prior to tapeout. This paper will investigate the metal layers of a structure designed to emulate an advanced node logic circuit that uses a CFET transistor. The structure will be corrected with OPC, and each layer will be simulated to generate a large (100) set of stochastic patterns at multiple process conditions in focus, overlay, and exposure. Each of these patterns will then be etched in a modeled process and metalized with copper. Finally, resistance and capacitance measurements will be generated from circuit simulations. The output data will then be used to update the lithography process or the pattern to improve through process performance including electrical characteristics." @default.
• W4367307939 created "2023-04-29" @default.
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• W4367307939 date "2023-04-28" @default.
• W4367307939 modified "2023-09-26" @default.
• W4367307939 title "Electrical analysis of a stochastically simulated 2 nm node electrical test structure" @default.
• W4367307939 doi "https://doi.org/10.1117/12.2658780" @default.
• W4367307939 hasPublicationYear "2023" @default.
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