# US 11,113,445 B2 · Hotspot detection based on litho-aware machine learning > Agents: this data is available as JSON from the patentgrep API and MCP server — see https://patentgrep.com/llms.txt - Number: US 11,113,445 B2 - Document ID: US11113445B2 - Status: Granted - Kind: B2 - Application number: 16/135,236 - Filed: 2018-09-19 - Published: 2019-03-21 - Granted: 2021-09-07 - Earliest claimed: 2017-09-20 - Assignees: Siemens Industry Software Inc. - Applicants: Mentor Graphics Corporation - Inventors: Jea Woo Park; Juan Andres Torres Robles - Web page: https://patentgrep.com/patent/US11113445B2 ## CPC - G06F 30/398: Design verification or optimisation, e.g. using design rule check [DRC], layout versus schematics [LVS] or finite element methods [FEM] (optical proximity correction [OPC] design processes G03F1/36) - G03F 7/70433: {Layout for increasing efficiency or for compensating imaging errors, e.g. layout of exposure fields for reducing focus errors; Use of mask features for increasing efficiency or for compensating imaging errors} - G03F 7/705: {Modelling or simulating from physical phenomena up to complete wafer processes or whole workflow in wafer productions} - G06F 30/18: Network design, e.g. design based on topological or interconnect aspects of utility systems, piping, heating ventilation air conditioning [HVAC] or cabling (circuit design at the physical level G06F30/39; network planning tools for wireless communication networks H04W16/18) - G06N 20/00: Machine learning - G06N 20/10: using kernel methods, e.g. support vector machines [SVM] - G06F 30/20: Design optimisation, verification or simulation (optimisation, verification or simulation of circuit designs G06F30/30) - G06F: ELECTRIC DIGITAL DATA PROCESSING (computer systems based on specific computational models G06N) - 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) - G06N: COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS ## Abstract Aspects of the disclosed technology relate to techniques of hotspot detection. Pinching-type hotspot candidates and bridging-type hotspot candidates are first identified in the layout design based on predetermined criteria. Simulation is then performed to derive aerial image intensity values for a plurality of sites on each of the pinching-type and bridging-type hotspot candidates. Pinching-type hotspots are determined from the pinching-type hotspot candidates based on one or more machine learning models for pinching-type hotspots, and bridging-type hotspots are determined from the bridging-type hotspot candidates based on one or more machine learning models for bridging-type hotspots. The input vector for the machine learning models is the aerial image intensity values for the plurality of sites. ## Claims ### Claim 1 A method, executed by at least one processor of a computer, comprising: receiving a layout design; identifying pinching-type hotspot candidates and bridging-type hotspot candidates in the layout design based on predetermined criteria; performing simulation to derive aerial image intensity values for a plurality of sites on each of the pinching-type hotspot candidates and for a plurality of sites on each of the bridging-type hotspot candidates; determining pinching-type hotspots from the pinching-type hotspot candidates based on one or more machine learning models for pinching-type hotspots and bridging-type hotspots from the bridging-type hotspot candidates based on one or more machine learning models for bridging-type hotspots, the input for the one or more machine learning models for pinching-type hotspots being the aerial image intensity values for the plurality of sites on each of the pinching-type hotspot candidates, the input for the one or more machine learning models for bridging-type hotspots being the aerial image intensity values for the plurality of sites on each of the bridging-type hotspot candidates; storing information of the pinching-type hotspots and the bridging-type hotspots; and thereafter finalizing the layout design to repair the pinching-type hotspots and the bridging-type hotspots, and manufacturing a circuit according to the finalized layout design using a photolithographic process. ### Claim 2 (depends on 1) The method recited in claim 1, wherein the one or more machine learning models for pinching-type hotspots comprise a machine learning model for horizontal pinching-type hotspots and a machine learning model for vertical pinching-type hotspots, the one or more machine learning models for bridging-type hotspots comprise a machine learning model for horizontal bridging-type hotspots and a machine learning model for vertical bridging-type hotspots, the pinching-type hotspot candidates are separated into a group of horizontal pinching-type hotspot candidates and a group of vertical pinching-type hotspot candidates, and the bridging-type hotspot candidates are separated into a group of horizontal bridging-type hotspot candidates and a group of vertical bridging-type hotspot candidates. ### Claim 3 (depends on 1) The method recited in claim 1, wherein the predetermined criteria may be set according to design checking rules. ### Claim 4 (depends on 1) The method recited in claim 1, wherein the one or more machine learning models for pinching-type hotspots and the one or more machine learning models for bridging-type hotspots are C-type Support Vector Machine models. ### Claim 5 (depends on 1) The method recited in claim 1, wherein a number of the plurality of sites is greater than 8 and less than 20. ### Claim 6 One or more non-transitory computer-readable media storing computer-executable instructions for causing one or more processors to perform a method, the method comprising: receiving a layout design; identifying pinching-type hotspot candidates and bridging-type hotspot candidates in the layout design based on predetermined criteria; performing simulation to derive aerial image intensity values for a plurality of sites on each of the pinching-type hotspot candidates and for a plurality of sites on each of the bridging-type hotspot candidates; determining pinching-type hotspots from the pinching-type hotspot candidates based on one or more machine learning models for pinching-type hotspots and bridging-type hotspots from the bridging-type hotspot candidates based on one or more machine learning models for bridging-type hotspots, the input for the one or more machine learning models for pinching-type hotspots being the aerial image intensity values for the plurality of sites on each of the pinching-type hotspot candidates, the input for the one or more machine learning models for bridging-type hotspots being the aerial image intensity values for the plurality of sites on each of the bridging-type hotspot candidates; storing information of the pinching-type hotspots and the bridging-type hotspots; and finalizing the layout design to repair the pinching-type hotspots and the bridging-type hotspots, wherein a circuit is thereafter manufactured according to the finalized layout design using a photolithographic process. ### Claim 7 (depends on 6) The one or more non-transitory computer-readable media recited in claim 6, wherein the one or more machine learning models for pinching-type hotspots comprise a machine learning model for horizontal pinching-type hotspots and a machine learning model for vertical pinching-type hotspots, the one or more machine learning models for bridging-type hotspots comprise a machine learning model for horizontal bridging-type hotspots and a machine learning model for vertical bridging-type hotspots, the pinching-type hotspot candidates are separated into a group of horizontal pinching-type hotspot candidates and a group of vertical pinching-type hotspot candidates, and the bridging-type hotspot candidates are separated into a group of horizontal bridging-type hotspot candidates and a group of vertical bridging-type hotspot candidates. ### Claim 8 (depends on 6) The one or more non-transitory computer-readable media recited in claim 6, wherein the predetermined criteria may be set according to design checking rules. ### Claim 9 (depends on 6) The one or more non-transitory computer-readable media recited in claim 6, wherein the one or more machine learning models for pinching-type hotspots and the one or more machine learning models for bridging-type hotspots are C-type Support Vector Machine models. ### Claim 10 (depends on 6) The one or more non-transitory computer-readable media recited in claim 6, wherein a number of the plurality of sites is greater than 8 and less than 20. ### Claim 11 A system, comprising: one or more processors, the one or more processors programmed to perform a method, the method comprising: receiving a layout design; identifying pinching-type hotspot candidates and bridging-type hotspot candidates in the layout design based on predetermined criteria; performing simulation to derive aerial image intensity values for a plurality of sites on each of the pinching-type hotspot candidates and for a plurality of sites on each of the bridging-type hotspot candidates; determining pinching-type hotspots from the pinching-type hotspot candidates based on one or more machine learning models for pinching-type hotspots and bridging-type hotspots from the bridging-type hotspot candidates based on one or more machine learning models for bridging-type hotspots, the input for the one or more machine learning models for pinching-type hotspots being the aerial image intensity values for the plurality of sites on each of the pinching-type hotspot candidates, the input for the one or more machine learning models for bridging-type hotspots being the aerial image intensity values for the plurality of sites on each of the bridging-type hotspot candidates; storing information of the pinching-type hotspots and the bridging-type hotspots; and finalizing the layout design to repair the pinching-type hotspots and the bridging-type hotspots, wherein a circuit is thereafter manufactured according to the finalized layout design using a photolithographic process. ### Claim 12 (depends on 11) The system recited in claim 11, wherein the one or more machine learning models for pinching-type hotspots comprise a machine learning model for horizontal pinching-type hotspots and a machine learning model for vertical pinching-type hotspots, the one or more machine learning models for bridging-type hotspots comprise a machine learning model for horizontal bridging-type hotspots and a machine learning model for vertical bridging-type hotspots, the pinching-type hotspot candidates are separated into a group of horizontal pinching-type hotspot candidates and a group of vertical pinching-type hotspot candidates, and the bridging-type hotspot candidates are separated into a group of horizontal bridging-type hotspot candidates and a group of vertical bridging-type hotspot candidates. ### Claim 13 (depends on 11) The system recited in claim 11, wherein the predetermined criteria may be set according to design checking rules. ### Claim 14 (depends on 11) The system recited in claim 11, wherein the one or more machine learning models for pinching-type hotspots and the one or more machine learning models for bridging-type hotspots are C-type Support Vector Machine models. ### Claim 15 (depends on 11) The system recited in claim 11, wherein a number of the plurality of sites is greater than 8 and less than 20.