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Functional fault diagnosis at board-level is desirable for high-volume production since it improves product yield. However, to ensure diagnosis accuracy and effective board repair, a large number of syndromes must be used. Therefore, the diagnosis cost can be prohibitively high due to the increase in diagnosis time and the complexity of syndrome collection/analysis. We propose an adaptive diagnosis method based on decision trees (DTs). Faulty components are classified according to the discriminative ability of the syndromes in DT training. The diagnosis procedure is constructed as a binary tree, with the most discriminative syndrome as the root and final repair suggestions are available as the leaf nodes of the tree. The syndrome to be collected in the next step is determined based on the observations of syndromes collected thus far in the diagnosis procedure. The number of syndromes required for diagnosis can also be significantly reduced compared to the number of syndromes used for system training. Diagnosis results for two complex boards from industry, currently in volume production, and additional synthetic data highlight the effectiveness of the proposed approach.

Three-dimensional (3D) integration based on through-silicon-vias (TSVs) is rapidly gaining traction for industry adoption. However, manufacturing processes for TSVs have been shown to introduce new failure mechanisms. In particular, thermo-mechanical stress and electromigration introduce reliability threats for TSVs, e.g., voids and interfacial cracks, which can lead to hard-to-predict timing errors on critical paths with TSVs, thereby resulting in accelerated chip failure in the field. Burn-in for screening latent defects during manufacturing is expensive and its effectiveness for new TSV defect types has yet to be thoroughly characterized. We describe a reconfigurable in-field repair solution that is able to effectively tolerate latent TSV defects through the judicious use of spares. The proposed solution includes a reconfigurable repair architecture that enables spare TSV sharing between TSV grids, and the corresponding in-field repair algorithms. The effectiveness and efficiency of our proposed solution is evaluated using 3D benchmark designs.

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