Fault Injection with a New Flavor: Memetic Algorithms Make a Difference

Author(s):  
Stjepan Picek ◽  
Lejla Batina ◽  
Pieter Buzing ◽  
Domagoj Jakobovic
Author(s):  
Rommel Estores ◽  
Karo Vander Gucht

Abstract This paper discusses a creative manual diagnosis approach, a complementary technique that provides the possibility to extend Automatic Test Pattern Generation (ATPG) beyond its own limits. The authors will discuss this approach in detail using an actual case – a test coverage issue where user-generated ATPG patterns and the resulting ATPG diagnosis isolated the fault to a small part of the digital core. However, traditional fault localization techniques was unable to isolate the fault further. Using the defect candidates from ATPG diagnosis as a starting point, manual diagnosis through fault Injection and fault simulation was performed. Further fault localization was performed using the ‘not detected’ (ND) and/or ‘detected’ (DT) fault classes for each of the available patterns. The result has successfully deduced the defect candidates until the exact faulty net causing the electrical failure was identified. The ability of the FA lab to maximize the use of ATPG in combination with other tools/techniques to investigate failures in detail; is crucial in the fast root cause determination and, in case of a test coverage, aid in having effective test screen method implemented.


Author(s):  
T. Kiyan ◽  
C. Boit ◽  
C. Brillert

Abstract In this paper, a methodology based upon laser stimulation and a comparison of continuous wave and pulsed laser operation will be presented that localizes the fault relevant sites in a fully functional scan chain cell. The technique uses a laser incident from the backside to inject soft faults into internal nodes of a master-slave scan flip-flop in consequence of localized photocurrent. Depending on the illuminated type of the transistors (n- or p-type), injection of a logic ‘0’ or ‘1’ into the master or the slave stage of a flip-flop takes place. The laser pulse is externally triggered and can easily be shifted to various time slots in reference to clock and scan pattern. This feature of the laser diode allows triggering the laser pulse on the rising or the falling edge of the clock. Therefore, it is possible to choose the stage of the flip-flop in which the fault injection should occur. It is also demonstrated that the technique is able to identify the most sensitive signal condition for fault injection with a better time resolution than the pulse width of the laser, a significant improvement for failure analysis of integrated circuits.


Author(s):  
Etienne Boespflug ◽  
Cristian Ene ◽  
Laurent Mounier ◽  
Marie-Laure Potet

2021 ◽  
Vol 120 ◽  
pp. 114116
Author(s):  
Xiaolu Hou ◽  
Jakub Breier ◽  
Dirmanto Jap ◽  
Lei Ma ◽  
Shivam Bhasin ◽  
...  

Mathematics ◽  
2021 ◽  
Vol 9 (5) ◽  
pp. 506
Author(s):  
Jorge Daniel Mello-Román ◽  
Adolfo Hernández ◽  
Julio César Mello-Román

Kernel partial least squares regression (KPLS) is a non-linear method for predicting one or more dependent variables from a set of predictors, which transforms the original datasets into a feature space where it is possible to generate a linear model and extract orthogonal factors also called components. A difficulty in implementing KPLS regression is determining the number of components and the kernel function parameters that maximize its performance. In this work, a method is proposed to improve the predictive ability of the KPLS regression by means of memetic algorithms. A metaheuristic tuning procedure is carried out to select the number of components and the kernel function parameters that maximize the cumulative predictive squared correlation coefficient, an overall indicator of the predictive ability of KPLS. The proposed methodology led to estimate optimal parameters of the KPLS regression for the improvement of its predictive ability.


Author(s):  
Andres Perez-Celis ◽  
Corbin Thurlow ◽  
Michael Wirthlin

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