Abstract: A system and a method may be provided for performing user authentication. The system comprises a processor and a memory coupled to the processor. The memory has stored therein instructions executable by the processor to configure the system to generate a multi-dimensional grid password for a user account, transmit the multi-dimensional grid password to a client device associated with the user account, and iteratively generate a policy for each of a plurality of user authentication sessions. The multi-dimensional grid password comprises a set of cells having corresponding one or more identifiers. The policy for each of the plurality of user authentication sessions comprises a unique set of identifiers from the one or more identifiers of each of the set of cells, and a policy for a session from the plurality of user authentication sessions causes to generate a subset of the multi-dimensional grid password for user authentication during the session.

Abstract: Embodiments are directed to a machine learning engine that determines training documents and validation documents from a plurality of documents. The machine learning engine may determine attributes associated with the documents. In response to receiving a request to predict attribute values of a selected document the machine learning engine may train a plurality of ML models to predict the attribute values based on the training documents and the attributes and associate the trained ML models with an accuracy score. The machine learning engine may determine candidate ML models from the trained ML models based on the training accuracy scores. The machine learning engine may evaluate and rank the candidate ML models based on the request and the validation documents. The machine learning engine may generate confirmed ML models based on the ranked candidate ML models such that the confirmed ML models may answer the request.

Abstract: Embodiments are directed to representing documents using document keys. Documents that include one or more clauses may be provided. Each clause type for the one or more clauses in documents may be determined based on one or more classification models. One or more clause identifiers may be associated with the one or more clauses based on one or more clause types of each clause. A document key may be generated for each document based on an ordered collection of the one or more clauses included in each document such that each clause identifier may be positioned in the document key based on an order of its location in a corresponding clause of a document. The documents may be analyzed based on evaluations of one or more document keys corresponding to the documents. One or more reports may be generated based on one or more results of the analysis.

Abstract: Embodiments are directed to representing documents using document keys. Documents that include one or more clauses may be provided. Each clause type for the one or more clauses in documents may be determined based on one or more classification models. One or more clause identifiers may be associated with the one or more clauses based on one or more clause types of each clause. A document key may be generated for each document based on an ordered collection of the one or more clauses included in each document such that each clause identifier may be positioned in the document key based on an order of its location in a corresponding clause of a document. The documents may be analyzed based on comparisons of one or more document keys corresponding to the documents. One or more reports may be generated based on one or more results of the analysis.

Abstract: Embodiments are directed to a machine learning engine that determines training documents and validation documents from a plurality of documents. The machine learning engine may determine attributes associated with the documents. In response to receiving a request to predict attribute values of a selected document the machine learning engine may train a plurality of ML models to predict the attribute values based on the training documents and the attributes and associate the trained ML models with an accuracy score. The machine learning engine may determine candidate ML models from the trained ML models based on the training accuracy scores. The machine learning engine may evaluate and rank the candidate ML models based on the request and the validation documents. The machine learning engine may generate confirmed ML models based on the ranked candidate ML models such that the confirmed ML models may answer the request.

Abstract: Embodiments are directed to managing documents over a network. A machine learning (ML) engine analyzes a plurality of documents associated with actions that were performed previously. The ML engine determines critical events associated with the performance of the actions based on the plurality documents. The ML engine generates ML models based on the critical events to compute risk values that may be associated with the critical events. In response to a request to compute risk values associated with pending actions, the ML engine determines documents that are associated with the pending actions based on the request. The ML engine determines the critical events associated with pending actions based on the documents. The ML engine employs the ML models to generate the risk values based on the documents and the critical events. The ML engine provides the risk values in response to the request.

Abstract: Embodiments are directed to a machine learning engine that determines training documents and validation documents from a plurality of documents. The machine learning engine may determine attributes associated with the documents. In response to receiving a request to predict attribute values of a selected document the machine learning engine may train a plurality of ML models to predict the attribute values based on the training documents and the attributes and associate the trained ML models with an accuracy score. The machine learning engine may determine candidate ML models from the trained ML models based on the training accuracy scores. The machine learning engine may evaluate and rank the candidate ML models based on the request and the validation documents. The machine learning engine may generate confirmed ML models based on the ranked candidate ML models such that the confirmed ML models may answer the request.

Abstract: Embodiments are directed to managing documents over a network. A machine learning (ML) engine analyzes a plurality of documents associated with actions that were performed previously. The ML engine determines critical events associated with the performance of the actions based on the plurality documents. The ML engine generates ML models based on the critical events to compute risk values that may be associated with the critical events. In response to a request to compute risk values associated with pending actions, the ML engine determines documents that are associated with the pending actions based on the request. The ML engine determines the critical events associated with pending actions based on the documents. The ML engine employs the ML models to generate the risk values based on the documents and the critical events. The ML engine provides the risk values in response to the request.

Abstract: Embodiments are directed to managing documents over a network. A machine learning (ML) engine analyzes a plurality of documents associated with actions that were performed previously. The ML engine determines critical events associated with the performance of the actions based on the plurality documents. The ML engine generates ML models based on the critical events to compute risk values that may be associated with the critical events. In response to a request to compute risk values associated with pending actions, the ML engine determines documents that are associated with the pending actions based on the request. The ML engine determines the critical events associated with pending actions based on the documents. The ML engine employs the ML models to generate the risk values based on the documents and the critical events. The ML engine provides the risk values in response to the request.

Abstract: Embodiments are directed to managing documents over a network. A machine learning (ML) engine analyzes a plurality of documents associated with actions that were performed previously. The ML engine determines critical events associated with the performance of the actions based on the plurality documents. The ML engine generates ML models based on the critical events to compute risk values that may be associated with the critical events. In response to a request to compute risk values associated with pending actions, the ML engine determines documents that are associated with the pending actions based on the request. The ML engine determines the critical events associated with pending actions based on the documents. The ML engine employs the ML models to generate the risk values based on the documents and the critical events. The ML engine provides the risk values in response to the request.

Abstract: Embodiments are directed to a machine learning engine that determines training documents and validation documents from a plurality of documents. The machine learning engine may determine attributes associated with the documents. In response to receiving a request to predict attribute values of a selected document the machine learning engine may train a plurality of ML models to predict the attribute values based on the training documents and the attributes and associate the trained ML models with an accuracy score. The machine learning engine may determine candidate ML models from the trained ML models based on the training accuracy scores. The machine learning engine may evaluate and rank the candidate ML models based on the request and the validation documents. The machine learning engine may generate confirmed ML models based on the ranked candidate ML models such that the confirmed ML models may answer the request.

Abstract: A method of fabricating an integrated circuit includes depositing a first dielectric material onto a semiconductor surface of a substrate having a gate stack thereon including a gate electrode on a gate dielectric. The first dielectric material is etched to form sidewall spacers on sidewalls of the gate stack. A top surface of the first dielectric material is chemically converted to a second dielectric material by adding at least one element to provide surface converted sidewall spacers. The second dielectric material is chemically bonded across a transition region to the first dielectric material.

Abstract: Embodiments are directed to managing documents where clauses in a document may be identified. Evaluations of the clauses may be provided based on evaluators and machine learning (ML) models that assign each of the clauses to a category and a confidence score. Actions associated with the clauses may be monitored including updates to content of the clauses. Inconsistent evaluations associated with the clauses be identified. The ML models may be retrained based on the content of the clauses associated with the inconsistent evaluations.

Abstract: Embodiments are directed to managing documents where clauses in a document may be identified. Evaluations of the clauses may be provided based on evaluators and machine learning (ML) models that assign each of the clauses to a category and a confidence score. Actions associated with the clauses may be monitored including updates to content of the clauses. Inconsistent evaluations associated with the clauses be identified. The ML models may be retrained based on the content of the clauses associated with the inconsistent evaluations.

Abstract: An integrated circuit containing MOS transistors may be formed using a split carbon co-implantation. The split carbon co-implant includes an angled carbon implant and a zero-degree carbon implant that is substantially perpendicular to a top surface of the integrated circuit. The split carbon co-implant is done at the LDD and halo implant steps.

Abstract: At least one method, apparatus and system are disclosed for forming a fin field effect transistor (finFET) having doping region self-aligned with a fin reveal position. A plurality of fins of a transistor is formed. A nitride cap layer on the plurality of fins is formed. An N-type doped region in a first portion of the plurality of fins. A P-type doped region in a second portion of the plurality of fins. A shallow trench isolation (STI) fill process for depositing an STI material on the plurality of fins. A fin reveal process for removing the STI material to a predetermined level. A cap strip process for removing the nitride cap layer for forming a fin reveal position that is self-aligned with the P-type and N-type doped regions.

Abstract: At least one method, apparatus and system are disclosed for forming a fin field effect transistor (finFET) having doping region self-aligned with a fin reveal position. A plurality of fins of a transistor is formed. A nitride cap layer on the plurality of fins is formed. An N-type doped region in a first portion of the plurality of fins. A P-type doped region in a second portion of the plurality of fins. A shallow trench isolation (STI) fill process for depositing an STI material on the plurality of fins. A fin reveal process for removing the STI material to a predetermined level. A cap strip process for removing the nitride cap layer for forming a fin reveal position that is self-aligned with the P-type and N-type doped regions.

Abstract: A method of fabricating an integrated circuit includes depositing a first dielectric material onto a semiconductor surface of a substrate having a gate stack thereon including a gate electrode on a gate dielectric. The first dielectric material is etched to form sidewall spacers on sidewalls of the gate stack. A top surface of the first dielectric material is chemically converted to a second dielectric material by adding at least one element to provide surface converted sidewall spacers. The second dielectric material is chemically bonded across a transition region to the first dielectric material.

Abstract: A method of fabricating an integrated circuit includes depositing a first dielectric material onto a semiconductor surface of a substrate having a gate stack thereon including a gate electrode on a gate dielectric. The first dielectric material is etched to form sidewall spacers on sidewalls of the gate stack. A top surface of the first dielectric material is chemically converted to a second dielectric material by adding at least one element to provide surface converted sidewall spacers. The second dielectric material is chemically bonded across a transition region to the first dielectric material.

Abstract: A method of fabricating an integrated circuit includes depositing a first dielectric material onto a semiconductor surface of a substrate having a gate stack thereon including a gate electrode on a gate dielectric. The first dielectric material is etched to form sidewall spacers on sidewalls of the gate stack. A top surface of the first dielectric material is chemically converted to a second dielectric material by adding at least one element to provide surface converted sidewall spacers. The second dielectric material is chemically bonded across a transition region to the first dielectric material.