Automated Test Scheduling using Adaptive Hybrid Prediction Technique

Author(s):  
Sarmishta Sarangarajan ◽  
B Sai Shruthi
PsycCRITIQUES ◽  
2006 ◽  
Vol 51 (7) ◽  
Author(s):  
Peter Merenda

Author(s):  
Carl M. Nail

Abstract Dice must often be removed from their packages and reassembled into more suitable packages for them to be tested in automated test equipment (ATE). Removing bare dice from their substrates using conventional methods poses risks for chemical, thermal, and/or mechanical damage. A new removal method is offered using metallography-based and parallel polishing-based techniques to remove the substrate while exposing the die to minimized risk for damage. This method has been tested and found to have a high success rate once the techniques are learned.


2017 ◽  
Vol 86 (1) ◽  
pp. 52-55
Author(s):  
Mitsuru OHATA ◽  
Hiroto SHOJI ◽  
Seiichiro TSUTSUMI ◽  
Tomokazu SANO

2020 ◽  
pp. 1-13
Author(s):  
Gokul Chandrasekaran ◽  
P.R. Karthikeyan ◽  
Neelam Sanjeev Kumar ◽  
Vanchinathan Kumarasamy

Test scheduling of System-on-Chip (SoC) is a major problem solved by various optimization techniques to minimize the cost and testing time. In this paper, we propose the application of Dragonfly and Ant Lion Optimization algorithms to minimize the test cost and test time of SoC. The swarm behavior of dragonfly and hunting behavior of Ant Lion optimization methods are used to optimize the scheduling time in the benchmark circuits. The proposed algorithms are tested on p22810 and d695 ITC’02 SoC benchmark circuits. The results of the proposed algorithms are compared with other algorithms like Ant Colony Optimization, Modified Ant Colony Optimization, Artificial Bee Colony, Modified Artificial Bee Colony, Firefly, Modified Firefly, and BAT algorithms to highlight the benefits of test time minimization. It is observed that the test time obtained for Dragonfly and Ant Lion optimization algorithms is 0.013188 Sec for D695, 0.013515 Sec for P22810, and 0.013432 Sec for D695, 0.013711 Sec for P22810 respectively with TAM Width of 64, which is less as compared to the other well-known optimization algorithms.


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