# US 2018/0023950 A1 · Analyzing Root Causes of Process Variation in Scatterometry Metrology > Agents: this data is available as JSON from the patentgrep API and MCP server — see https://patentgrep.com/llms.txt - Number: US 2018/0023950 A1 - Document ID: US20180023950A1 - Status: Published application - Kind: A1 - Application number: 15/329,618 - Filed: 2016-11-01 - Published: 2018-01-25 - Earliest claimed: 2016-02-25 - Assignees: KLA-Tencor Corporation - Applicants: KLA-Tencor Corporation - Inventors: Tal Marciano; Michael E. Adel; Mark Ghinovker; Barak Bringoltz; Dana Klein; Tal Itzkovich; Vidya Ramanathan; Janay Camp - Web page: https://patentgrep.com/patent/US20180023950A1 ## CPC - G01B 11/272: {using photoelectric detection means} - G03F 7/70616: {Monitoring the printed patterns} - G03F 7/70633: {Overlay, i.e. relative alignment between patterns printed by separate exposures in different layers, or in the same layer in multiple exposures or stitching} - H10P 74/203: {Structural properties, e.g. testing or measuring thicknesses, line widths, warpage, bond strengths or physical defects} - H10P 74/23: {characterised by multiple measurements, corrections, marking or sorting processes} - G01B: MEASURING LENGTH, THICKNESS OR SIMILAR LINEAR DIMENSIONS; MEASURING ANGLES; MEASURING AREAS; MEASURING IRREGULARITIES OF SURFACES OR CONTOURS - G03F: PHOTOMECHANICAL PRODUCTION OF TEXTURED OR PATTERNED SURFACES, e.g. FOR PRINTING, FOR PROCESSING OF SEMICONDUCTOR DEVICES; MATERIALS THEREFOR; ORIGINALS THEREFOR; APPARATUS SPECIALLY ADAPTED THEREFOR; (phototypographic composing devices B41B; photosensitive materials or processes for photographic purposes G03C; electrophotography, sensitive layers or processes therefor G03G) - H10P: GENERIC PROCESSES OR APPARATUS FOR THE MANUFACTURE OR TREATMENT OF DEVICES COVERED BY CLASS H10 ## Abstract Method, metrology modules and RCA tool are provided, which use the behavior of resonance region(s) in measurement landscapes to evaluate and characterize process variation with respect to symmetric and asymmetric factors, and provide root cause analysis of the process variation with respect to process steps. Simulations of modeled stacks with different layer thicknesses and process variation factors may be used to enhance the analysis and provide improved target designs, improved algorithms and correctables for metrology measurements. Specific targets that exhibit sensitive resonance regions may be utilize to enhance the evaluation of process variation. ## Claims ### Claim 1 A method comprising: deriving a landscape from simulation and/or from a plurality of scatterometry measurements, wherein the landscape comprises an at least partially continuous dependency of at least one metrology metric on at least one parameter, identifying, in the derived landscape, at least one resonance region that corresponds to a resonance of optical illumination in a measured location, analyzing a dependency of the identified at least one resonance region on specified changes in the at least one parameter, and deriving from the analysis an estimation of process variation. ### Claim 2 (depends on 1) The method of claim 1, wherein the at least one parameter comprises any of: an illumination wavelength, a pupil location, a measurement parameter, and a process parameter. ### Claim 3 (depends on 1) The method of claim 1, wherein the at least one metrology metric comprises at least one of an overlay, an overlay variation metric and an inaccuracy metric defined with respect to the overlay. ### Claim 4 (depends on 3) The method of claim 3, wherein the overlay is calculated as OVL({right arrow over (p)})=((D1({right arrow over (p)})+D2({right arrow over (p)}))/(D1({right arrow over (p)})−D2({right arrow over (p)})))·f0, with {right arrow over (p)} representing a pupil pixel, f0 denoting a designed offset, and with D1 and D2 denoting, corresponding to opposite designed offsets, differences between signal intensities of opposing orders measured at pupil pixels which are rotated by 180° with respect to each other. ### Claim 5 (depends on 1) The method of claim 1, further comprising detecting a movement of the at least one resonance region across a simulated and/or measured pupil image as the at least one parameter, and estimating symmetric process variation therefrom. ### Claim 6 (depends on 1) The method of claim 1, further comprising detecting a sign change in the at least one resonance region, and estimating asymmetric process variation therefrom. ### Claim 7 (depends on 6) The method of claim 6, wherein the sign change comprises a reversal of an inaccuracy gradient upon changing the at least one parameter ### Claim 8 (depends on 5) The method of claim 5, further comprising: mapping the identified at least one resonance region over a plurality of measurement locations on a wafer, characterizing at least one spatial relation in the mapping, and identifying a root cause for the process variation according to the characterization. ### Claim 9 (depends on 8) The method of claim 8, further comprising carrying out the root cause identification in parallel to metrology measurements to predict metrology excursions. ### Claim 10 (depends on 9) The method of claim 9, further comprising adjusting a metrology measurements recipe according to the predicted metrology excursions. ### Claim 11 (depends on 1) The method of claim 1, further comprising analyzing the derived landscape with respect to different sites on a wafer. ### Claim 12 (depends on 11) The method of claim 11, further comprising using a reference wafer to derive the estimation of process variation in the sites with respect thereto. ### Claim 13 (depends on 11) The method of claim 11, further comprising adjusting a sampling for a metrology measurements recipe to carry out the metrology measurements on sites across the wafer, which correspond to a same region of the landscape. ### Claim 14 (depends on 1) A computer program product comprising a non-transitory computer readable storage medium having computer readable program embodied therewith, the computer readable program configured to carry out the method of claim 1. ### Claim 15 (depends on 1) A metrology tool configured to carry out the method of claim 1. ### Claim 16 A RCA (root cause analysis) tool associated with at least one computer processor and configured to receive process related parameters and measurement data, and derive therefrom a landscape and at least one resonance region in the landscape, wherein the landscape comprises a dependency of at least one metrology metric on at least one parameter and the at least one resonance region corresponds to a resonance of optical illumination in a measured location, the RCA tool further configured to evaluate a process variation from an analysis of changes in the at least one resonance region in the landscape with respect to the received process related parameters and measurement data. ### Claim 17 (depends on 16) The RCA tool of claim 16, further configured to map changes in the at least one resonance region over at least one wafer. ### Claim 18 (depends on 17) The RCA tool of claim 17, wherein the mapping is carried out with respect to at least one of: a wafer, a process-split wafer, and a plurality of wafers of a lot. ### Claim 19 (depends on 16) The RCA tool of claim 16, further configured to provide correctables based on the evaluated process variation. ### Claim 20 (depends on 16) The RCA tool of claim 16, further configured to derive a root cause analysis of evaluated process variation. ### Claim 21 (depends on 20) The RCA tool of claim 20, further configured to simulate a modeled stack with various homogenous thickness variations to derive patterns of process variation, and to relate the derived patterns to specific process-related root causes. ### Claim 22 (depends on 21) The RCA tool of claim 21, further configured to incorporate in the simulation asymmetric process variation factors comprising at least one of: grating asymmetry, topography, cell-to-cell variation, target noise, and any process variation that breaks the asymmetry inside the modeled stack. ### Claim 23 (depends on 22) The RCA tool of claim 22, further configured to quantify an expected inaccuracy for different types of process variations and suggest modifications to at least one of: target designs, metrology algorithms and measurement recipes.