The radiation sensitivity of memory cells increases dramatically as CMOS manufacture technology scales down; therefore, the reliability of memories has become a challenge. 3D technology has gained attention for having several advantages compared to the 2D counterpart, such as high integration density, high performance, low power, and high communication speed. Although several studies are targeting 3D memories, the effects on reliability using this technology have received little attention. This work introduces Line Product Code (LPC), a modified product code-based Error Correction Code (ECC) that uses both Hamming and parity in both rows and columns to implement reliable 3D memories. We implemented two lightweight LPC-based decoding algorithms in interleaved (LPCa-I) and non-interleaved (LPCa) versions, which allowed us to analyze LPC through a set of simulation cases that considers four severity levels of error incidence. The experimental results showed the effectiveness of the LPC-based algorithms, reaching correction rates of up 2.3 times higher compared to other Hamming-based algorithms.

Battery mathematical models can be employed to predict its behavior under various charging and discharging conditions, and in some applications, they are key elements for the successes of the design. In this paper, it is analyzed a lead-acid battery model for voltage and lifetime estimation. The chosen model synthesis is based on an electrical equivalent circuit, and has the features that allow it to be used in the calculation of the remaining capacity. The lifetime model employs an adaptive update mechanism during the simulation such that the battery performance is reduced depending on the operating conditions. This paper objective is to generalize the analyzed mathematical system to be used in any lead-acid battery. Both the model and the estimation algorithm are evaluated with two batteries with same voltage and nominal capacity. The generalization of equation is made by the addition of three new parameters to minimize an objective function based on the least squares error. The study is based on a specific scenario that makes the battery reached the end of its lifetime. With the addition of new parameters, the RMS error between real capacity and calculated capacity are evaluated.