Static Random Access Memory (SRAM) chips undergo several types of stress in the field. Existing work has
concentrated primarily on humidity and thermal stress; there has been relatively little emphasis on signal density stress
prediction. Objectives of this study were to (1) explore the impact of signal density stress on SRAM functionality, (2)
observe thermal profile differences under signal density stress over time, (3) predict stress levels using artificial neural
network models, and (4) develop a generic methodology for signal density stress prediction. An 8051 programming
board containing an SRAM chip was used. Two kinds of signal density stress were investigated - varying the content
written to memory, and varying signal frequency in accessing SRAM through flash memory. Preliminary experiments
suggest that both types of stress impact the SRAM thermal profile. Thermal profile data were used to build back
propagation neural network models; 70% of the data was used to build the models and 30% was used for testing.
Various neural network training functions and topologies were used to predict chip stress level given thermal profile
data. Data from both the die area and the entire chip were used. For both types of stress, using data from the die area in
a network with a 3-3-1 topology yielded the lowest average error rate - 1.3% for data content stress level prediction and
7.6 % for signal frequency stress level prediction. The trainRP function resulted in a lower error rate than other training
functions that were evaluated.