Neural Network Defect Prediction Patents 2026 — PatSnap Eureka
Neural Network Defect Prediction: 2026 Patent Landscape
Predictive quality control using neural networks is replacing reactive inspection with proactive risk scoring across semiconductor, medical, software, and advanced manufacturing domains. This dataset spans filings from 2004 to 2026 across at least 14 distinct assignees and 11 jurisdictions.
From Statistical Process Control to AI-Native Quality Prognostics
Predictive quality control neural networks ingest process parameters, sensor telemetry, historical defect records, or code change metrics, outputting probabilistic defect risk scores, failure-state forecasts, or yield predictions — enabling intervention before defects occur. The field divides into four coherent sub-domains: manufacturing process quality prognostics, software defect prediction, hardware component failure prediction, and IC design hotspot prediction.
LYNCEUS SAS explicitly notes that traditional process control techniques such as Statistical Process Control are now too limited to reliably anticipate defects, as they cannot follow multiple machine parameters simultaneously. PDF Solutions SAS, National Cheng Kung University, and LYNCEUS SAS each frame their inventions as replacements for linear SPC baselines in complex semiconductor and manufacturing environments.
The dataset spans filings from 2004 — National Cheng Kung University’s foundational quality prognostics patent in TW/US — through active 2026 filings from Siemens Industry Software (WO) and Zhejiang Innovative Chip Integration (CN). Three distinct development phases are visible: a Foundational Phase (2004–2012), an Expansion Phase (2015–2021), and a Maturation and Specialization Phase (2022–2026).
In this dataset, innovation is moderately concentrated: Accenture Global Services Limited leads with 10+ filings and Microsoft Technology Licensing follows with 8 filings in retrieved records, but domain leadership is fragmented — semiconductor QA, medical equipment, and IC design each have distinct leading players across separate jurisdictions.
Technology Clusters and Jurisdiction Concentration
Patent activity in this dataset clusters around four technology areas, with semiconductor/IC design and hardware failure prediction accounting for the majority of named filings. Jurisdiction analysis reveals US and CN as the two most active filing destinations, with WO (PCT) filings indicating broad international protection intent.
Patent Filings by Technology Sub-Domain (Dataset Snapshot)
Semiconductor/IC design and hardware failure prediction together account for the largest share of named filings in this dataset, with software defect prediction and manufacturing process quality prognostics representing distinct but smaller clusters.
↗ Click bars to exploreFiling Activity by Phase and Jurisdiction (Dataset Snapshot)
US and CN collectively account for the highest share of filing activity in this dataset, with CN filings concentrated in 2023–2026 reflecting a recent acceleration from Chinese assignees in chip yield and software defect prediction.
↗ Click bars to exploreKey Application Domains Across Semiconductor, Medical, Software, and Manufacturing
Predictive quality control neural networks in this dataset are deployed across at least six distinct application domains, from IC design layout hotspot prediction to clinical analyzer failure forecasting and software commit-level defect ranking. The following domains represent the most patent-active areas in retrieved records.
Semiconductor & IC Design
The densest application cluster in this dataset, including filings from National Cheng Kung University (2004–2009, TW/US), PDF Solutions SAS (2008, US), LYNCEUS SAS (2022–2023, WO/US), Synopsys (2021, US/TW), and Siemens Industry Software (2025–2026, US/WO). Siemens’ March 2026 WO filing integrates per-element failure rate data into the training loss function for prioritized physical inspection of IC hotspots. LYNCEUS SAS explicitly positions its system to supersede SPC limitations in multi-parameter semiconductor wafer production.
Semiconductor QualityMedical Diagnostics Equipment
F. Hoffmann-La Roche AG and Roche Diagnostics Operations hold a multi-jurisdiction family across WO (2018), EP (2019, 2023), and US (2019, 2024), targeting prediction of failure states in automated clinical analyzers using calibration and quality control data. Koninklijke Philips N.V. filed X-ray tube degradation prediction using cumulative exposure fingerprints across EP (2022), WO (2022), and US (2023, 2025). Pathology Watch Inc. also filed neural-network-driven cancer pathology laboratory information systems in WO and US (2025).
Medical Hardware PredictionSoftware Engineering & DevOps
Microsoft Technology Licensing’s Feature Deployment Readiness Prediction family covers 8 filings across US, WO, and IN (2021–2024), applying telemetry-based quality metric prediction for staged software rollouts. Tata Consultancy Services Limited’s January 2026 IN filing extends defect prediction upstream to requirements-level traceability, linking requirements, test cases, and production incidents to forecast requirement-induced failures. A 2019 literature record describes deep learning preprocessing of ten numerical commit metrics for effort-aware just-in-time defect prediction.
Software Defect PredictionAdvanced Manufacturing & Lithography
Sichuan University’s April and July 2025 CN filings introduce multi-fidelity physics-informed neural networks embedding manufacturing defect effects as physical constraints, using Wasserstein GANs for uncertainty quantification. Carl Zeiss SMT GmbH’s September 2025 WO filing distributes N copies of a neural network across N lithography apparatuses, training locally on each apparatus’s operational data and aggregating updates centrally. Jabil Inc. filed a functional test failure prediction engine integrating product design, bill of materials, and manufacturing design inputs (WO 2022, US 2024).
Manufacturing Failure PredictionKey Patent Assignees in Neural Network Defect Prediction (Retrieved Records)
In retrieved records, Accenture Global Services Limited accounts for 10+ filings across US, CA, AU, SG, EP, and HK jurisdictions, while Microsoft Technology Licensing LLC accounts for 8 filings across US, WO, and IN — together representing the highest individual filing volumes in this dataset, though domain leadership across semiconductor, medical, and IC design sub-fields is held by distinct specialist assignees.
Top Assignees by Filing Count in Retrieved Records (Dataset Snapshot)
↗ Click bars to exploreAccenture Global Services Limited
Accenture holds 10+ filings in retrieved records, spanning US, CA, AU, SG, EP, and HK jurisdictions filed between 2015 and 2017, representing an early cross-jurisdictional portfolio covering network node failure prediction. The system extracts performance metrics from multiple data sources, trains and validates a ranked ensemble of models to predict fail conditions in network nodes, then selects the highest-rated model for deployment. Multiple filings remain active across CA, SG, and HK jurisdictions as of this dataset snapshot.
United StatesMicrosoft Technology Licensing LLC
Microsoft Technology Licensing holds 8 filings in retrieved records across US (6 filings), WO, and IN jurisdictions, with the Feature Deployment Readiness Prediction family filed from December 2021 through 2024. The family applies telemetry-based quality metric prediction for staged software rollouts, forecasting deployment readiness scores before feature releases. Filings span WO (2021), US (2022, 2024), and IN jurisdictions, indicating intent for broad international coverage in the software QA prediction space.
United StatesForward-Looking Signals from 2024–2026 Filings
Four forward-looking directions are visible from filings dated 2024–2026 in this dataset, ranging from physics-hybrid neural architectures to federated fleet training and requirements-level defect prediction. These represent the earliest feasible intervention points in the manufacturing and software development lifecycles.
Physics-Informed Neural Networks for Defect-Induced Fatigue Life
Sichuan University’s April and July 2025 CN filings introduce multi-fidelity physics-informed neural networks (PINNs) that embed manufacturing defect effects as physical constraints within the neural architecture. The approach uses Wasserstein generative adversarial networks for uncertainty quantification and transfer learning across data fidelity levels. This represents a shift from purely data-driven to physics-hybrid approaches for structural quality prediction in metallic component manufacturing.
Federated Neural Network Training Across Lithography Equipment Fleets
Carl Zeiss SMT GmbH’s September 2025 WO filing distributes N copies of a neural network across N individual lithography apparatuses, training locally on each apparatus’s operational data and aggregating updates centrally. This applies federated learning principles to capital equipment quality prediction, allowing fleet-wide model improvement without centralizing sensitive operational data. The system targets prediction of ageing effects in lithography optical elements.
Statistical Process Control vs. Neural Network Predictive Quality Control
Click any row to explore further.
| Dimension | Statistical Process Control (SPC) | Neural Network Predictive QC |
|---|---|---|
| Parameter Handling | Limited to monitoring a small number of parameters simultaneously | Handles multiple machine parameters simultaneously (LYNCEUS SAS, 2023) |
| Defect Detection Timing | Reactive — detects defects after they occur via control chart breach | Proactive — outputs probabilistic defect risk scores before defects materialize |
| Data Requirements | Assumes normally distributed, homogeneous process data | Handles heterogeneous, imbalanced production data (LYNCEUS SAS, 2023) |
| Foundational Complexity | Linear modeling approach — insufficient for complex multi-parameter environments | Supports multi-branch, multi-fidelity, and physics-hybrid architectures (Siemens, Sichuan Univ., 2025) |
| Virtual Metrology | Not supported — requires physical measurement of each part | Enables quality prediction without physical measurement via virtual metrology (Natl. Cheng Kung Univ., 2004–2009) |
| Upstream Prediction Reach | Applied at production stage only | Extendable to IC design layout, requirements specification, and capital equipment fleet stages (Synopsys 2021, TCS 2026, Carl Zeiss 2025) |
| Representative Assignees | IBM (linear QA modeling patents, US 2020, 2022) | Siemens Industry Software, Synopsys, LYNCEUS SAS, Roche, Accenture, Microsoft, Tata Consultancy Services |
Frequently Asked Questions: Neural Network Defect Prediction Patents
Virtual metrology uses equipment parameters as neural network inputs to predict product quality without physically measuring every part. National Cheng Kung University pioneered this with a conjecture-plus-prediction dual-model architecture for semiconductor and TFT-LCD manufacturing, first filed in Taiwan in 2004 and granted in the US in 2005–2009.
In this dataset, Accenture Global Services Limited holds 10+ filings spanning US, CA, AU, SG, EP, and HK jurisdictions, all related to network node failure prediction filed between 2015 and 2017. Microsoft Technology Licensing follows with 8 filings across US, WO, and IN.
Physics-informed neural networks (PINNs) embed manufacturing defect effects as physical constraints within the neural architecture, rather than relying solely on historical data patterns. Sichuan University’s 2025 CN filings use multi-fidelity PINNs with Wasserstein GANs for uncertainty quantification and transfer learning across data fidelity levels, targeting metallic component fatigue life prediction.
In this dataset, US is the dominant filing jurisdiction across all assignees. CN is the second most active, with filings concentrated in 2023–2026 from domestic Chinese assignees covering chip yield prediction, software defect prediction, and fatigue life prediction. WO (PCT) filings appear for Roche, Jabil, LYNCEUS, Philips, and Carl Zeiss, signaling broad international protection intent.
Siemens Industry Software’s March 2026 WO filing integrates per-circuit-element failure rate data directly into the training loss function, allowing the model to prioritize high-consequence defect sites over low-risk geometric violations. Earlier methods such as Synopsys’ 2021 ANN approach focused on linking predicted failure modes to reticle enhancement technique recipe selection, without failure-rate weighting in the training objective.
Requirements-level defect prediction extends neural network quality prediction upstream to the requirements specification phase, forecasting which requirements will generate downstream defects and production incidents through traceability linkage across software releases. Tata Consultancy Services Limited filed this approach in January 2026 via IN, linking requirements, test cases, and production incidents to predict requirement-induced failures.
Data and insights on this page are based on a limited patent and literature dataset and are for reference only. Figures may not represent the complete technology landscape.