This paper presents a model-fitting framework to correlate the on-chip measured ring-oscillator counts to the chip's maximum operating speed. This learned model can be included in an auto test equipment (ATE) software to predict the chip speed for speed binning. Such a speed-binning method can avoid the use of applying any functional test and, hence, result in a third-order test time reduction with a limited portion of chips placed into a slower bin compared with the conventional functional-test binning. This paper further presents a novel built-in self-speed-binning system, which embeds the learned chip-speed model with a built-in circuit such that the chip speed can be directly calculated on-chip without going through any offline ATE software, achieving a fourth-order test-time reduction compared with the conventional speed binning. The experiments were conducted based on 360 test chips of a 28-nm, 0.9 V, 1.6-GHz mobile-application system-on-chip.
|Number of pages||13|
|Journal||IEEE Transactions on Very Large Scale Integration (VLSI) Systems|
|State||Published - 1 May 2016|
- Machine learning
- rind oscillator
- speed binning.