ISBN 978-1-4244-8193-4International audienceThe high cost of analog circuit testing has sparked off intensified efforts to identify robust and low-cost alternative tests that could effectively replace the standard specification-based tests. Nevertheless, the current practice is still specification-based testing. One of the primary reasons is the lack of tools to evaluate in advance the indirect costs (e.g. parametric test escape and yield loss) associated with alternative tests. To this end, in this paper, we present a method to estimate test escape and yield loss that occur as a result of replacing one costly specification test by one low-cost alternative test. This evaluation is performed at the design or test development stage with parts per million (PPM) accuracy. The method is based on extreme value theory and on a fast simulation technique of extreme events called statistical blockade

The deployment of alternative, low-cost RF test methods in industry has been, to date, rather limited. This is due to the potentially impaired ability to identify device pass/fail labels when departing from traditional specification test. By relying on alternative tests, pass/fail labels must be derived indirectly through new test limits defined for the alternative tests, which may incur error in the form of test escapes or yield loss. Clearly, estimating these test metrics as early as possible in the test development process is key to the success of an alternative test approach. In this work, we employ a test metrics estimation technique based on non-parametric kernel density estimation to obtain such early estimates, and, for the first time, demonstrate a real-world case study of test metric estimation efficiency at parts-per-million levels. To achieve this, we employ a set of more than 1 million RF devices fabricated by Texas Instruments, which have been tested with both traditional specification tests as well as alternative, low-cost On-chip RF Built-in Tests, or "ORBiTs".

This paper discusses the generation of informationrich, arbitrarily-large synthetic data sets which can be used to (a) efficiently learn tests that correlate a set of low-cost measurements to a set of device performances and (b) grade such tests with parts per million (PPM) accuracy. This is achieved by sampling a non-parametric estimate of the joint probability density function of measurements and performances. Our case study is an ultra-high frequency receiver front-end and the focus of the paper is to learn the mapping between a lowcost test measurement pattern and a single pass/fail test decision which reflects compliance to all performances. The small fraction of devices for which such a test decision is prone to error are identified and retested through standard specification-based test. The mapping can be set to explore thoroughly the tradeoff between test escapes, yield loss, and percentage of retested devices.

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