Test & Diagnosis Complexity Reduction using GCNs

By Ishan Aphale

There are mainly two ways of accelerating the verification and testing process: 1) Reducing the test set redundancy; 2) Reducing the complexity of the testing, verification and diagnosis process. To reduce the test set redundancy or to optimize the generation of test instances, coverage-directed test generation has been studied for a long time, which can be aided by lots of ML algorithms. Recently, test set redundancy reduction of analog/RF design or even the test of semiconductor technology have raised a lot of attention, and more ML methods are applied to solve these problems. As for reducing the verification & test complexity, there are studies that adopt low-cost tests for analog/RF design, and some other studies that focus on fast bug classification and localization.

Recently, GCNs are used to solve the observation point insertion problem for the testing stage. Inserting an observation point between the output of module 1 and the input of module 2 will make the test results of module 1 observable and the test inputs of module 2 controllable. We can use GCN to insert fewer test observation points while maximizing the fault coverage. More specifically, the netlist is first mapped to a directed graph, in which nodes represent modules, and edges represent wires. Then, the nodes are labeled as easy-to-observe or difficult-to-observe, and a GCN classifier is trained. Compared with commercial test tools, this method based on GCN can reduce the observation points by 11% under similar fault coverage, and reduce the test pattern count by 6%. Note that compared with other studies discussed before, observation point insertion reduces the test complexity in a different way, by decoupling the test of different modules.

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