Predicate |
Object |
assignee |
http://rdf.ncbi.nlm.nih.gov/pubchem/patentassignee/MD5_8cf8d77ac0eff1767b22d2fb9445b64d |
classificationCPCAdditional |
http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/G06F30-20 |
classificationCPCInventive |
http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/H01L21-02351 http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/H01L21-306 http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/H01L21-31105 http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/H01L21-02205 http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/G06N20-00 http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/G06N10-00 http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/G06N99-005 http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/G06F30-20 http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/H01L21-324 http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/G06N99-002 http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/G06F17-5009 |
classificationIPCInventive |
http://rdf.ncbi.nlm.nih.gov/pubchem/patentipc/G06F17-50 http://rdf.ncbi.nlm.nih.gov/pubchem/patentipc/G06N99-00 |
filingDate |
2018-05-03-04:00^^<http://www.w3.org/2001/XMLSchema#date> |
inventor |
http://rdf.ncbi.nlm.nih.gov/pubchem/patentinventor/MD5_a85b87c2d4821b06528e8a5237449d74 http://rdf.ncbi.nlm.nih.gov/pubchem/patentinventor/MD5_23355b3a13c0bcaad7ac0875eb3e2c17 http://rdf.ncbi.nlm.nih.gov/pubchem/patentinventor/MD5_b29e5429a4f08e4648420425dd9be9e7 http://rdf.ncbi.nlm.nih.gov/pubchem/patentinventor/MD5_e281f9a4fd35335b1c77e5f0e8bfb07a http://rdf.ncbi.nlm.nih.gov/pubchem/patentinventor/MD5_ff7a9827027deae680d3a0a47bd8f300 |
publicationDate |
2019-11-07-04:00^^<http://www.w3.org/2001/XMLSchema#date> |
publicationNumber |
US-2019340316-A1 |
titleOfInvention |
Predicting etch characteristics in thermal etching and atomic layer etching |
abstract |
Etch in a thermal etch reaction is predicted using a machine learning model. Chemical characteristics of an etch process and associated energies in one or more reaction pathways of a given thermal etch reaction are identified using a quantum mechanical simulation. Labels indicative of etch characteristics may be associated with the chemical characteristics and associated energies of the given thermal etch reaction. The machine learning model can be trained using chemical characteristics and associated energies as independent variables and labels as dependent variables across many different etch reactions of different types. When chemical characteristics and associated energies for a new thermal etch reaction are provided as inputs in the machine learning model, the machine learning model can accurately predict etch characteristics of the new thermal etch reaction as outputs. |
isCitedBy |
http://rdf.ncbi.nlm.nih.gov/pubchem/patent/US-11054793-B2 http://rdf.ncbi.nlm.nih.gov/pubchem/patent/CN-113517033-A http://rdf.ncbi.nlm.nih.gov/pubchem/patent/US-2022359656-A1 http://rdf.ncbi.nlm.nih.gov/pubchem/patent/CN-113807524-A |
priorityDate |
2018-05-03-04:00^^<http://www.w3.org/2001/XMLSchema#date> |
type |
http://data.epo.org/linked-data/def/patent/Publication |