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US 2026/0252064 A1Published application
Systems and methods for deep learning-driven neurosymbolic techniques for correlation discovery, causal analysis, and prescriptive recommendations with knowledge-based reasoning
AssigneeArizona Board of Regents on behalf of Arizona State University
InventorsMukund Shenoy, Farhad Ameri
Life of this patent
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Abstract
A system implements a hybrid AI framework which integrates symbolic reasoning with data-driven approaches to enhance defect prediction and risk management in semiconductor manufacturing. The framework combines rule-based logic and deep learning to handle multi-modal data from design, process, equipment, and material sources (such as FMEAs, specifications, metrology, and sensor data) and provides insights into hidden patterns in the data, identification of root causes for observed issues, and actionable outputs tailored to domain-specific requirements.
Claims20
A computer-implemented method for defect diagnosis using neurosymbolic techniques, comprising:
- ingesting, by at least one processor, multimodal manufacturing data comprising specification requirements and measured observations for a manufacturing process;
- mapping the multimodal manufacturing data to an ontological knowledge graph that encodes domain entities and causal dependencies for the manufacturing process;
- executing one or more physics-informed rules expressed as queries over the ontological knowledge graph to evaluate conformance between the measured observations and the specification requirements and, responsive to detecting at least one deviation, inserting into the ontological knowledge graph at least one failure-cause assertion corresponding to the at least one deviation and at least one directed causal edge linking a node corresponding to the at least one failure-cause assertion to a node corresponding to an observed defect;
- assembling, by traversing the ontological knowledge graph, a causal chain that links the observed defect to at least one failure cause through one or more intermediate mechanisms represented in the knowledge graph, the causal chain comprising nodes corresponding to the failure-cause assertions and the observed defect and edges corresponding to the directed causal edges;
- and
- generating a remedial instruction based on the causal chain by identifying, for at least one failure cause in the causal chain, at least one modification to at least one process parameter associated with the manufacturing process, the remedial instruction causing a manufacturing system to modify at least one process parameter for subsequent execution of the manufacturing process to reduce incidence of the observed defect.
3 more elements
A system for defect diagnosis using neurosymbolic techniques, comprising:
- at least one processor;
- and
- at least one non-transitory memory storing instructions that, when executed by the at least one processor, cause the processor to:
- ingest multimodal manufacturing data comprising specification requirements and measured observations for a manufacturing process;
- map the multimodal manufacturing data to an ontological knowledge graph that encodes domain entities and causal dependencies for the manufacturing process;
- execute one or more physics-informed rules derived from the ontological knowledge graph to evaluate conformance between the measured observations and the specification requirements and, responsive to detecting at least one deviation, insert into the ontological knowledge graph at least one failure-cause assertion and at least one causal edge linking the deviation to an observed defect;
- assemble a causal chain that links the observed defect to at least one failure cause through one or more intermediate mechanisms represented in the ontological knowledge graph;
- and
- generate at least one remedial instruction based on the causal chain, the at least one remedial instruction identifying at least one modification to at least one process parameter of a manufacturing system for reducing incidence of the observed defect.
6 more elements
A non-transitory computer-readable medium storing instructions that, when executed by one or more processors, cause the one or more processors to:
- ingest multimodal operational data comprising specification requirements and measured observations for at least one process that produces a physical product;
- map the multimodal operational data to an ontological knowledge graph that encodes domain entities and causal dependencies for the at least one process;
- execute one or more physics-informed rules derived from the ontological knowledge graph to evaluate conformance between the measured observations and the specification requirements and, responsive to detecting at least one deviation, insert into the ontological knowledge graph at least one failure-cause assertion and at least one causal edge linking the deviation to an observed outcome;
- assemble a causal chain that links the observed outcome to at least one failure cause through one or more intermediate mechanisms represented in the ontological knowledge graph;
- and
- generate a neurosymbolic diagnostic output based on the causal chain, the neurosymbolic diagnostic output including at least one remedial recommendation that identifies at least one candidate modification to at least one process parameter associated with the at least one process.
3 more elements
Dependencies are inferred from the claim text.