Double Hamming distance‐based 2D reordering method for scan‐in power reduction and test pattern compression

Abstract

A double Hamming distance-based 2D reordering method is proposed for a test set with don't care bits (Xs) to reduce scan-in power and compress test patterns. In this method, the rows and columns in the test set are reordered sequentially so that similar rows or columns are aggregated together. Being different from other reordering-based methods, the authors' method reorders every two rows or columns with more identical bits closer in the whole reordering process, where more Xs are clustered. Each X is then replaced with 0 or 1 using minimum transition filling scheme to minimise '1-0' and '0-1' logical state transitions. A small number of the logic state transitions cause low switching activity, which not only reduces scan-in power consumption but also facilitates code-based test pattern compression. Experimental results show that their proposed method achieves peak and average power reductions of 18.01 and 12.97% compared to the state-of-the-art method, respectively. Furthermore, the compression ratio of alternating shifted frequency directed run-length coding is improved to 73.76% in average utilising their proposed method.

An analysis of low power testing using K ‐means clustering with reordering approach

“…K-means clustering approach is an iterative process and repeats the iteration until the mean distance between cluster centre and test data points becomes stable. So the similarity between the test vectors in the Even though the hamming distance parameter is similar in the existing and proposed method, the proposed method improves the average test power and peak test power by 88% and 35%, respectively, compared to the existing 2D reordering method [1].…”

“…Even though the hamming distance parameter is similar in the existing and proposed method, the proposed method improves the average test power and peak test power by 88% and 35%, respectively, compared to the existing 2D reordering method [1].…”

“…Experimental Analysis: Existing test vector reordering techniques focused on test data compression. The power of the compressed test set only is analysed in the existing approach [1,9,10]. During scan-based testing, the compressed test set should be decompressed and loaded into the scan chain for testing the IC.…”

“…Hamming distance-based reordering and 2D reordering techniques are the existing methods focused on test vectors in the test set only. In Double hamming distance-based 2D reordering, two dimensions of the test set (rows and columns) are considered for reordering the test vectors for effective compression [1]. Sokal and Sneath similarity coefficient is used for reordering the test set to improve the compression [2].…”

Group Decision-Making Method of Entry Policy During a Pandemic

“…In recent years, the method of measuring distance has been applied to various fields [16] . Many scholars have quantified the consensus degree of expert ranking based on distance methods, such as Hamming distance [17,18] and Euclidean distance [19,20] . Yet, distance-based methods may sometimes fail to adequately reflect consensus in group decision-making.…”