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Resist-slope aware modeling for mask process correction applications
Author(s): Rachit Sharma; Ingo Bork; Kushlendra Mishra; ShiZhi Lyu; Linna Cong; Mingjing Tian; Malavika Sharma; Bhardwaj Durvasula; Nageswara Rao; Peter Buck
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Paper Abstract

This work presents our investigations on a new resist-slope kernel for Mask Process Correction (MPC) applications, specifically modeling the contribution (including linear and higher-order) of the resist image slope to the overall etch bias. Mask Process Correction (MPC) models with different complexities, i.e., varying number of kernels, were calibrated and compared against each other for model accuracy, layout correction run-time and dose-dependent residual trends. The results demonstrate that using the resist-slope kernel with a simpler model can allow for up to 40 percent lower correction run-time (compared to complex models) without a major degradation of the overall model accuracy. Hence, this paper presents the resist-slope kernel as a valuable addition to MPC modeling techniques, especially for situations where conventional methods are not sufficient to meet the accuracy or run time requirements.

Paper Details

Date Published: 26 September 2019
PDF: 8 pages
Proc. SPIE 11148, Photomask Technology 2019, 111480G (26 September 2019); doi: 10.1117/12.2536529
Show Author Affiliations
Rachit Sharma, Mentor Graphics (India) Private Ltd. (India)
Ingo Bork, Mentor, a Siemens Business (United States)
Kushlendra Mishra, Mentor Graphics (India) Private Ltd. (India)
ShiZhi Lyu, Mentor Graphics (Shanghai) Electronic Technology Co. Ltd. (China)
Linna Cong, Semiconductor Manufacturing International (Shanghai) Corp. (China)
Mingjing Tian, Semiconductor Manufacturing International (Shanghai) Corp. (China)
Malavika Sharma, Mentor Graphics (India) Private Ltd. (India)
Bhardwaj Durvasula, Mentor Graphics (India) Private Ltd. (India)
Nageswara Rao, Mentor Graphics (India) Private Ltd. (India)
Peter Buck, Mentor, a Siemens Business (United States)


Published in SPIE Proceedings Vol. 11148:
Photomask Technology 2019
Jed H. Rankin; Moshe E. Preil, Editor(s)

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