Evaluating functional fault criticality of structural faults for circuit testing
AssigneeDuke University
InventorsKrishnendu Chakrabarty, Arjun Chaudhuri, Jonti Talukdar
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Abstract
A system for evaluating fault criticality using machine learning includes a first machine learning module that is trained on a subset of a circuit and used for evaluating whether a node in a netlist of the entire circuit is a critical node, and a second machine learning module specialized to minimize classification errors in nodes predicted as benign. A generative adversarial network can be used to generate synthetic test escape data to supplement data used to train the second machine learning module.
Claims12
A system for evaluating fault criticality using machine learning, the system comprising:
- a storage device;
- a first machine learning module for evaluating fault criticality of a node, wherein the first machine learning module is configured to:
- receive a feature set of topology features and data features for a plurality of nodes of a target hardware architecture having an applied domain-specific use-case;
- receive an entire netlist for the target hardware architecture having the applied domain-specific use-case;
- and
- output a dataset of predicted benign nodes and a dataset of predicted critical nodes for the entire netlist, the dataset of predicted critical nodes being output to the storage device and stored at the storage device;
- and
- a second machine learning module specialized to minimize test escapes from the first machine learning module, wherein the second machine learning module is configured to:
- receive the dataset of predicted benign nodes generated by the first machine learning module;
- and
- output a dataset of final predicted benign nodes and a dataset of predicted test escape nodes, the dataset of predicted test escape nodes being output to the storage device and stored at the storage device as part of the dataset of predicted critical nodes at the storage device.
A method for training a system to evaluate for fault criticality, the method comprising:
- receiving fault simulation data for a target hardware architecture having an applied domain-specific use-case, wherein the fault simulation data corresponds to a subset of an entire circuit to be tested, the fault simulation data comprising known critical nodes and known benign nodes;
- receiving a feature set of topology features and data features for a plurality of nodes of a target hardware architecture having an applied domain-specific use-case;
- training a first machine learning module using the fault simulation data to predict fault criticality of a particular node of the subset of the entire circuit based on associated topology and data features for the particular node with respect to the feature set of the topology features and the data features, the training producing a set of predicted benign nodes and a set of predicted critical nodes;
- and
- training a second machine learning module using the fault simulation data and at least the set of predicted benign nodes produced by the first machine learning module.
Dependencies are inferred from the claim text.