|標題:||Statistical Framework and Built-In Self-Speed-Binning System for Speed Binning Using On-Chip Ring Oscillators|
Chao, Mango C. -T.
Department of Electronics Engineering and Institute of Electronics
|關鍵字:||Machine learning;rind oscillator;speed binning|
|摘要:||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 builtin 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.|
|期刊:||IEEE TRANSACTIONS ON VERY LARGE SCALE INTEGRATION (VLSI) SYSTEMS|
|Appears in Collections:||Articles|