Tradeoffs in multichip module yield and cost with known good die probability and repair
Abstract The influences of known good die (KGD) probability, repair, and module testing on multichip module (MCM) yield and cost have been modeled and systematically analyzed. The current work extends our previous efforts on MCMs with single (one KGD probability) and dual (two KGD probabilities) populations to modules containing complex, multiple chip populations. Most of the analysis is performed on modules with multiple populations in the range of three to five (i.e., three to five distinct chip types). In order to develop a total cost picture for an MCM versus the respective KGD probabilities of the underlying chip populations, it was necessary to develop new algorithms or modify previously developed algorithms for the following items: number of modules (necessary to ensure at least one MCM works), KGD chip cost, and chip repair/replacement costs. In order to visualize the results and simplify calculations, averages over the respective subpopulations have been employed. The combination of these models and algorithms produces cost values in multiple (KGD) probability space that contain optimum or minimal points. Associated with the cost minimums are specific KGD probabilities for each chip type in the module population. Thus, one only pays for improved KGD probability up to the values that minimize overall module cost. Repair has a direct and significant impact on overall module yield and cost, with the first repair providing the largest improvement in both yield and cost reduction.