A material failure forecasting system accesses historical failure data to forecasts future failures. The failure data of a material is analyzed using text processing techniques to identify failures and suspensions. The text processing techniques provide for identifying failures when fault words are associated with negations. A fault ontology establishes different failure modes that include primary, secondary and tertiary levels which enable identifying a sequence of failures. The failures thus identified are fitted to a data distribution selected from a plurality of data distributions. The parameters from the data distribution are used for simulating a demand profile for the material which considers interchangeability. Similarly failure data of the materials in an equipment can be analyzed and the reliability of the equipment can be estimated.

Provided are techniques for digital twin functional and non-functional simulation testing. An indication is received that digital twin functional and non-functional simulation testing is to start for an application being developed, where a first portion of code for the application has been developed and a second portion of the code for the application has not been developed. Application data and an application landscape are retrieved. The digital twin functional and non-functional simulation testing is performed to identify which functional and non-functional requirements are not being met by the first portion of the code. For the functional and non-functional requirements that are not being met, suggestions are provided for at least one of the first portion and the second portion to meet one or more of the functional and non-functional requirements. One or more of the suggestions are implemented.

A parallel analog circuit automatic optimization method based on genetic algorithm and machine learning comprises global optimization based on genetic algorithm and local optimization based on machine learning, with the global optimization and the local optimization performed alternately. The global optimization based on genetic algorithm utilizes parallel SPICE simulations to improve the optimization efficiency while guaranteeing the optimization accuracy, combined with parallel computing. The local optimization based on machine learning establishes a machine learning model near the global optimal point obtained by the global optimization, and uses the machine learning model to replace the SPICE simulator, thus reducing the time costs brought by a large number of simulations.

Systems and methods related to learning-based analyzers (both supervised and unsupervised) for mitigating latch-up in integrated circuits are provided. An example method includes obtaining latch-up data concerning at least one integrated circuit configured to operate under a range of temperature conditions, where the at least one integrated circuit comprises a core portion including at least a plurality of devices each having one or more structural features formed using a lithographic process, and an input/output portion. The method further includes training the learning-based system based on training data derived from the latch-up data and a first layout rule concerning a first spacing between the core portion and the input/output portion. The method further includes using the learning-based system generating a second layout rule concerning the first spacing between the core portion and the input/output portion, where the second layout rule is different from the first layout rule.

Methods, apparatus, systems and articles of manufacture are disclosed to simulate metastability for circuit design verification. An example apparatus includes an input handler to receive circuit design data indicative of a circuit design, a circuit modeler to generate a simulation model based on the circuit design data, a simulator to simulate operation of the circuit design based on the simulation model, a metastability injector to insert metastability logic into the simulation model during the simulation, and a metastability controller to control the metastability logic during the simulation.

A line diagnosis method includes, in a situation that a first interface of a first device is connected to a second interface of a second device, obtaining first interface information of the first interface and second interface information of the second interface, and diagnosing a line connection between the first device and the second device based on the first interface information and the second interface information.

A Toffoli magic state to be injected in preparation of a Toffoli gate may be prepared using a bottom-up approach. In the bottom-up approach, computational basis states are prepared in a fault tolerant manner using a STOP algorithm. The computational basis states are further used to prepare the Toffoli magic state. The STOP algorithm tracks syndrome outcomes and can be used to determine when to stop repeating syndrome measurements such that faults are guaranteed to be below a threshold level. Also, the STOP algorithm may be used in growing repetition code from a first code distance to a second code distance, such as for use in the computational basis states.

The present invention is a system and method for 3D engineering drawing extrapolation and automation incorporating Machine Learning (ML). The instant innovation receives a 3D computer model of a part to be manufactured, and automatically breaks the model into labelled surfaces capable of being attributed, assigned and represented by 2D drawings. One or more sub-processes receives data defining attributes of the 2D drawings and performs calculations to pre-determine drill-hole locations on a machine-ready part. The system then determines if there are unintended gaps, interferences, or other irregularities resident thereupon. The system creates a list of any irregularities and returns a punch list to a human user for correction. The system utilizes Amazon Web Services (AWS) to both perform data extracting and flattening of the 3D model and to select optimally-sized machine stock and optimize its orientation in relation to the manufacturing machine head.

A method for detecting an open/short circuit on a PCB design layout includes: reading PCB data of a to-be-checked PCB design layout, to output an image of each PCB layer included in the PCB design layout; performing a first connectivity analysis on the image of each PCB layer to classify pad patterns connected with each other in the same layer into a corresponding child network group; performing a second connectivity analysis to classify child network groups in which pad patterns connected by the same electroplated hole, into a corresponding parent network group; reading IPC netlist data of the PCB design layout, to obtain a netlist network group in which each pad pattern is; and determining whether a netlist network relationship of the pad patterns is consistent with a network relationship obtained after the second connectivity analysis in order to determine whether there is an open/short circuit.

In sensor data analytics, physics-based models generate high quality data. However, these models consume lot of time as they rely on physical simulations. On the other hand, generative learning takes much less time to generate data, and may be prone to error. Present disclosure provides system and method for generation of synthetic machine data for healthy and abnormal condition using hybrid of physics based and generative model-based approach. Finite Element Analysis (FEA) is used for simulating healthy and faulty parts in machinery with set of parameters and pre-condition(s). Small output data from FEA is fed into a generative model for generating synthesized data by learning data distribution knowledge and representing into latent space. Rule engine is built using statistical features wherein realistic bounds serve as faulty data indicators. Synthesized data which does not satisfies features bounds are discarded. Further, AI-based validation framework is used to analyze quality of synthesized data.