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US 2026/0252845 A1Published application
Systems and methods for neurosymbolic root-cause analysis and ranked triage
AssigneeArizona Board of Regents on behalf of Arizona State University
InventorMukund Shenoy
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 neurosymbolic defect diagnosis in manufacturing, comprising:
- ingesting, by at least one processor, manufacturing data for a product unit, the manufacturing data comprising measured observations and specification requirements for a manufacturing process;
- applying, by the at least one processor, the manufacturing data as input to a trained neural evidence model to generate probabilistic evidence including (i) a defect probability distribution, (ii) at least one stage-wise mechanism probability distribution, and (iii) a plurality of parameter-risk probabilities;
- selecting, based on the probabilistic evidence, (i) at least one defect hypothesis and (ii) at least one mechanism hypothesis;
- identifying, based on the plurality of parameter-risk probabilities, a bounded set of candidate parameter-violation hypotheses;
- instantiating, in a knowledge graph, semantic individuals corresponding to at least one defect hypothesis, at least one mechanism hypothesis, and the bounded set of candidate parameter-violation hypotheses;
- creating, for each instantiated semantic individual, a provenance-linked evidence object that stores at least a probabilistic value or confidence score derived from the probabilistic evidence and associates the probabilistic value or confidence score with at least one of (a) measurement context or (b) specification context, and provenance metadata identifying the trained neural evidence model and an inference context;
- executing, in view of the knowledge graph, one or more deterministic causal rules that connect at least one defect hypothesis to at least one candidate parameter-violation hypothesis through at least one mechanism hypothesis to form a plurality of causal chains, wherein the one or more deterministic causal rules do not use the probability values or confidence scores stored in the provenance-linked evidence objects to determine whether to form the plurality of causal chains;
- and
- ranking the plurality of causal chains based on the probability values stored in the provenance-linked evidence objects to produce an output comprising a ranked list of causal chains for the product unit.
6 more elements
A system for defect diagnosis using neurosymbolic techniques, comprising:
- a processor and memory storing instructions that, when executed by the processor, cause the processor to implement a neurosymbolic diagnosis pipeline comprising a neural layer and a semantic reasoning layer integrated via a conformance assessment interface, wherein the processor:
- ingests manufacturing data associated with a manufacturing process;
- implements the neural layer to quantify uncertainty from manufacturing observations defined in the manufacturing data to output, from the neural layer, probabilistic evidence signals including probability values associated with aspects of the manufacturing process;
- executes the semantic reasoning layer to instantiate semantic individuals for domain knowledge associated with the manufacturing process;
- creates for each of the semantic individuals, a provenance-linked evidence object that binds at least one probability value or confidence score to measurement context, specification context, and provenance metadata associated with the neural layer;
- forms a plurality of causal chains given the semantic individuals and in view of one or more deterministic causal rules, wherein the one or more deterministic causal rules do not use the probability values stored in the provenance-linked evidence objects to determine whether to form the plurality of causal chains;
- and
- ranks the plurality of causal chains based on the probability values stored in a respective provenance-linked evidence object to produce an output comprising a ranked list of causal chains configured for diagnosing the manufacturing process.
5 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 data associated with a manufacturing process;
- quantify, by a neural layer, uncertainty from manufacturing observations defined in the manufacturing data to output probabilistic evidence signals including probability values associated with aspects of the manufacturing process;
- instantiate, by a semantic reasoning layer, semantic individuals for domain knowledge associated with the manufacturing process;
- create, for each of the semantic individuals, a provenance-linked evidence object that binds at least one probability value or confidence score to measurement context, specification context, and provenance metadata associated with the neural layer;
- form a plurality of causal chains given the semantic individuals and in view of one or more deterministic causal rules, wherein the one or more deterministic causal rules do not use the probability values stored in the provenance-linked evidence objects to determine whether to form the plurality of causal chains;
- and
- rank the plurality of causal chains based on the probability values stored in a respective provenance-linked evidence object to produce a ranked list of causal chains configured for diagnosing the manufacturing process.
4 more elements
Dependencies are inferred from the claim text.