Automating defects simulation and fault modeling for SRAMs

The continues improvement in manufacturing process density for very deep sub micron technologies constantly leads to new classes of defects in memory devices. Exploring the effect of fabrication defects in future technologies, and identifying new classes of realistic functional fault models with their corresponding test sequences, is a time consuming task up to now mainly performed by hand. This paper proposes a new approach to automate this procedure. The proposed method exploits the capabilities of evolutionary algorithms to automatically identify faulty behaviors into defective memories and to define the corresponding fault models and relevant test sequences. Target defects are modeled at the electrical level in order to optimize the results to the specific technology and memory architecture

Genetic Defect Based March Test Generation for SRAM

The continuos shrinking of semiconductor's nodes makes semiconductor memories increasingly prone to electrical defects tightly related to the internal structure of the memory. Exploring the effect of fabrication defects in future technologies, and identifying new classes of functional fault models with their corresponding test sequences, is a time consuming task up to now mainly performed by hand. This paper pro- poses a new approach to automate this procedure exploiting a dedicated genetic algorithm

Framework for Fault Analysis and Test Generation in DRAMs

Abstract: With the increasing complexity of memory behavior, attempts are being made to come up with a methodical approach that employs electrical simulation to tackle the memory test problem. This paper describes a framework of algorithms and tools developed jointly by the Delft University of Technology and Infineon Technologies to systematically generate DRAM tests using Spice simulation. The proposed Spice-based test approach enjoys the advantage of being relatively inexpensive, yet highly accurate in describing the desired memory faulty behavior