This paper presents a Built-In-Self-Test (BIST) implementation of pseudo-random testing for Micro Electro-Mechanical Systems (MEMS). The technique is based on Impulse Response (IR) evaluation using Maximum-Length Sequences (MLS). We will demonstrate the use of this technique and move forward to find the signature that is defined as the necessary samples of the impulse response needed to carry out an efficient test. We will use Monte-Carlo simulations to find the set of all fault-free devices under test (DUT). This set defines the impulse response space and the signature space. A DUT will be judged faultfree according to its signature being inside or outside the boundaries of the signature space. Finally, the test quality will be evaluated as function of the probabilities of false acceptance and false rejection, yield and percentage of test escapes. According to these test metrics, the design parameters (length of the MLS and the precision of the analogue to digital converter ADC) will be derived.
The high cost for testing the analog blocks of a modern chip has sparked research efforts to replace the standard tests with less costly alternative tests. However, test engineers are rather reluctant to adopt alternative tests unless they are evaluated thoroughly before moving to production and they are proven to maintain test quality. This paper gives a comprehensive overview of statistical techniques based on density estimation for evaluating analog parametric test metrics during the test development phase. A large-scale simulation study is carried out for the first time with the aim to demonstrate these techniques in action.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.