An evaluation of the extrinsic cells number in a memory array using cross-correlation products and deconvolution: an instance of a microelectronics experimental inverse problem

Abstract

This work is devoted to a new, fast and efficient method of evaluating the number of marginal cells in a non-volatile electrical memory array. This extraction from experimental data is fundamentally an inverse problem. The method proposed here is based on simple cross-correlation functions and de-convoluting operations. With the microelectronics device dimension downscaling, the reliability of non-volatile electrical memory has become crucial and any marginal cell can compromise the functioning of the whole array (containing hundreds of thousands of elementary cells). A specific array called Cell Array Structure Test (CAST) has been developed as a useful characterization tool to statistically study retention and endurance performance with few experimental operations. However, this device cannot easily count the low number of failed cells among hundreds of thousands. That is why we had to develop a mathematical method to extract this major quantity from measurements. This method has been validated on an EEPROM CAST – 0.13 µm technology node, but it is extendable to all memory devices integrated in parallel array and more generally to any electrical measurement done in a similar configuration.