An Artificial Neuron Network Based Chip Health Assessment Framework for IC Recycling

In the past decade smart devices have become a major driving force behind the growth of semiconductor industry. The fierce competition for market shares and profits has rendered frequent release of new generations with fancier designs, more functionalities and better performance. On the other hand, the chips inside these smart devices are designed with lifetime typically spanning a few generations at least. As a result, many smart devices are thrown away with their chips still functioning. Conventional recycling business simply aims at recovering copper, silver, gold, palladium and other materials, and does not take into consideration the potentially functioning chips. We argue that a much better recycling framework should properly classify and bin the functioning chips from recycled smart devices for reuse so that additional profit can be generated and environment can be better protected. In this paper, we propose the concept of integrated circuit recycling, and demonstrate a statistical health assessment method using artificial neuron network (ANN) based search tree along with an optimal price-binning framework with low-cost measurements. Experimental results show that with the simple measurement of I_{ddq} and V_{min}, our health assessment can eliminate lifetime overestimation, while flat ANN has that up to 13%. In addition, our price-binning algorithm can obtain up to extra 51% profit compared with an intuitive maximum-likelihood based approach.