Improving fault coverage for random-pattern-resistant designs

December 07, 2015 // By Abhishek Mahajan
Improving fault coverage for random-pattern-resistant designs
Abhishek Mahajan considers how to Improve fault coverage for random-pattern-resistant designs.

Designs with LogicBIST exhibit random pattern resistance because of the random nature of LBIST vectors, thus leading to low fault coverage. To handle this, we insert test points with the help of random resistant fault analysis (RRFA). The computation of the fault detection capability of a design with LBIST is done with the help of fault simulation, which gives an estimate of “quality of test”. We discuss these in more detail below, along with techniques to increase fault detection in LBIST designs.

Fault targeting with LogicBIST

Test through LBIST is a pseudo random test unlike the production scan test which is more deterministic test. The scan vectors in LBIST are generated by a Pseudo Random Pattern Generator (PRPG) which generates pseudo random sequences. Whereas in the case of production scan testing the scan vectors are deterministically fed through the scan inputs through the Automatic Test Equipment (ATE).

Due to the random nature of LBIST test it is not always possible to test particular faults because of no direct control on what sequence of scan inputs is being shifted in the design. Problems arise when LBIST is implemented on designs which are combinational intense or have large combinational paths between the registers. These designs may become resistant to random patterns which means the probability of controlling some nodes randomly to a 0 or 1 value, or the probability of observing some nodes to a scan-register is low, assuming random and equally probable inputs being fed to the design.

Taking example of an AND gate in Figure 1 we calculate the probability of controlling the output of the gate to a value ‘1’. The diagram below shows the probability of each node getting a value of ‘1’ or ‘0’. Format P(1) / P(0).

Figure 1: Controllability for 2 input and gate

Figure 2 the probability of getting ‘0’ or ‘1’ value at the different nodes when the combinational depth is increased by

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