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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.

As technology node keeps scaling and design complexity keeps increasing, power distribution networks (PDNs) require more routing resource to meet IR-drop and electro-migration (EM) constraints. This paper presents a design flow to generate a PDN that can result in near-minimal overhead for the routing of the underlying standard cells while satisfying both IR-drop and EM constraints based on a given cell placement. The design flow relies on a machine-learning model to quickly predict the total wire length of global route associated with a given PDN configuration in order to speed up the search process. The experimental results based on various 28 nm industrial block designs have demonstrated the accuracy of the learned model for predicting the routing cost and the effectiveness of the proposed framework for reducing the routing cost of the final PDN.

This paper presents a single-test-input test-decompression scheme, named STSD, which utilize the technique of test-slice duplication to reduce the test-data volume as well as the signal transitions along scan paths. The encoding of STSD scheme focuses on maximizing the number of duplications made by a test-slice template. Mathematical models are also developed in this paper to estimate the compression ratio, test-application time, and scan-in transitions caused by STSD scheme, and in turn can further help designers to efficiently identify the best configuration of STSD scheme instead of going through a time-consuming simulation process. The experimental results based on large ISCAS and ITC benchmark circuits demonstrate the accuracy of the proposed mathematical models and the advantages of using STSD scheme.