| 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/G06N10-00
http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/G06F30-20
http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/H01L21-02205
http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/H01L21-324
http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/H01L21-31105
http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/H01L21-306
http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/H01L21-02351
http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/G06N20-00 |
| classificationIPCInventive |
http://rdf.ncbi.nlm.nih.gov/pubchem/patentipc/H01L21-02
http://rdf.ncbi.nlm.nih.gov/pubchem/patentipc/H01L21-306
http://rdf.ncbi.nlm.nih.gov/pubchem/patentipc/H01L21-311
http://rdf.ncbi.nlm.nih.gov/pubchem/patentipc/H01L21-324 |
| filingDate |
2019-04-23-04:00^^<http://www.w3.org/2001/XMLSchema#date> |
| inventor |
http://rdf.ncbi.nlm.nih.gov/pubchem/patentinventor/MD5_ff7a9827027deae680d3a0a47bd8f300
http://rdf.ncbi.nlm.nih.gov/pubchem/patentinventor/MD5_b29e5429a4f08e4648420425dd9be9e7
http://rdf.ncbi.nlm.nih.gov/pubchem/patentinventor/MD5_a85b87c2d4821b06528e8a5237449d74
http://rdf.ncbi.nlm.nih.gov/pubchem/patentinventor/MD5_e281f9a4fd35335b1c77e5f0e8bfb07a
http://rdf.ncbi.nlm.nih.gov/pubchem/patentinventor/MD5_23355b3a13c0bcaad7ac0875eb3e2c17 |
| publicationDate |
2019-11-07-04:00^^<http://www.w3.org/2001/XMLSchema#date> |
| publicationNumber |
WO-2019212810-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. |
| priorityDate |
2018-05-03-04:00^^<http://www.w3.org/2001/XMLSchema#date> |
| type |
http://data.epo.org/linked-data/def/patent/Publication |