On-board SRAM signal density stress prediction

Abstract

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.

Survey of thermography in electronics inspection

“…So far, we have found relatively little work in the area of developing real-time systems for defect or failure detection that can be deployed in a mass production environment other than the systems developed by the Air Force Air Logistics Center in Utah [7,9,15,21]. There has also been relatively little work reported in the area of predicting the life-time of electronic components and devices, other than the work by Hsieh [16][17][18][19][20]. For example, in a mass production environment, detection of BGA solder joint integrity defects (e.g., HIP defects) is still an open problem to be solved.…”

“…Additional performance measure variables include average heating rate and grayscale change rates over time. Previous research by Allred [15] and the Hsieh [16][17][18][19][20] suggests that heating rate and/or average heating rate are good indicators of component quality status. Grayscale change rates were used because the infrared camera captures images over time and-with appropriate gray scale calibration-the brightness of the images is proportional to temperature changes.…”

“…This non-contact technique has been applied in inspecting a variety of electronic board defects including open, short circuit, intermittent defects [5][6][7][8][9], solder joint integrity [10][11][12] and fault diagnosis [13][14][15]. More importantly, it has also been applied for electronics stress level prediction under temperature, voltage, signal density, and mechanical vibration stresses [16][17][18][19][20].…”

Thermal profiling techniques for electronics inspection

“…Previous research by Allred [15] and the Hsieh [16][17][18][19][20] suggests that heating rate and/or average heating rate are good indicators of component quality status. Grayscale change rates were used because the infrared camera captures images over time and-with appropriate gray scale calibration-the brightness of the images is proportional to temperature changes.…”