Special session: Hot topics: Statistical test methods

International audienceThe process of testing Integrated Circuits involves a huge amount of data: electrical circuit measurements, information from wafer process monitors, spatial location of the dies, wafer lot numbers, etc. In addition, the relationships between faults, process variations and circuit performance are likely to be very complex and non-linear. Test (and its extension to diagnosis) should be considered as a challenging highly dimensional multivariate problem.Advanced statistical data processing offers a powerful set of tools, borrowed from the fields of data mining, machine learning or artificial intelligence, to get the most out of this data. Indeed, these mathematical tools have opened a number of novel and interesting research lines within the field of IC testing.In this special session, prominent researchers in this field will share their views on this topic and present some of their last findings. The first talk will discuss the interest of likelihood prevalence in random fault simulation. The second talk will show how statistical data analysis can help diagnosing test efficiency. The third talk will deal with the reliability of Alternate Test of AMS-RF circuits. The fourth and last talk will address the idea of mining the test data for improving design manufacturing and even test itself

Evaluation of indirect measurement selection strategies in the context of analog/RF alternate testing

International audienceThis paper is in the field of Analog or RF integrated circuit testing. The conventional practice for testing those circuits relies on the measurement of the device-under-test (DUT) specifications. In order to reduce test costs, a promising approach, called indirect or alternate testing has been proposed. Its basic principle consists in using the correlation between the conventional analog/RF performances and some low-cost measurements, called Indirect Measurements (IMs), in order to estimate the analog/RF parameters without measuring directly them. The objective of this paper is to perform a comparative analysis of different IM selection strategies in order to define efficient alternate testing implementation. Efficiency is discussed in terms of model accuracy and predictions robustness. Results are illustrated on a Power Amplifier (PA) test vehicle for which we have experimental test data on 10,000 circuits