3 July 2015 Process monitoring using automatic physical measurement based on electrical and physical variability analysis
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Abstract
A fully automated silicon-based methodology for systematic analysis of electrical features is shown. The system was developed for process monitoring and electrical variability reduction. A mapping step was created by dedicated structures such as static-random-access-memory (SRAM) array or standard cell library, or by using a simple design rule checking run-set. The resulting database was then used as an input for choosing locations for critical dimension scanning electron microscope images and for specific layout parameter extraction then was input to SPICE compact modeling simulation. Based on the experimental data, we identified two items that must be checked and monitored using the method described here: transistor’s sensitivity to the distance between the poly end cap and edge of active area (AA) due to AA rounding, and SRAM leakage due to a too close N-well to P-well. Based on this example, for process monitoring and variability analyses, we extensively used this method to analyze transistor gates having different shapes. In addition, analysis for a large area of high density standard cell library was done. Another set of monitoring focused on a high density SRAM array is also presented. These examples provided information on the poly and AA layers, using transistor parameters such as leakage current and drive current. We successfully define “robust” and “less-robust” transistor configurations included in the library and identified unsymmetrical transistors in the SRAM bit-cells. These data were compared to data extracted from the same devices at the end of the line. Another set of analyses was done to samples after Cu M1 etch. Process monitoring information on M1 enclosed contact was extracted based on contact resistance as a feedback. Guidelines for the optimal M1 space for different layout configurations were also extracted. All these data showed the successful in-field implementation of our methodology as a useful process monitoring method.
© 2015 Society of Photo-Optical Instrumentation Engineers (SPIE)
Eitan N. Shauly, Shimon Levi, Ishai Schwarzband, Ofer Adan, Sergey Latinsky, "Process monitoring using automatic physical measurement based on electrical and physical variability analysis," Journal of Micro/Nanolithography, MEMS, and MOEMS 14(2), 021107 (3 July 2015). https://doi.org/10.1117/1.JMM.14.2.021107 . Submission:
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