The majority of transistors in a modern microprocessor are used to implement static random access memories (SRAM). Therefore, it is important to analyze the reliability of SRAM blocks. During the SRAM design, it is important to build in design margins to achieve an adequate lifetime. The two main wearout mechanisms that increase a transistor’s threshold voltage are bias temperature instability (BTI) and hot carrier injections (HCI). BTI and HCI can degrade transistors’ driving strength and further weaken circuit performance. In a microprocessor, first-level (L1) caches are frequently accessed, which make it especially vulnerable to BTI and HCI. In this chapter, the cache lifetimes due to BTI and HCI are studied for different cache configurations, namely, cache size, associativity, cache line size, and replacement algorithm. To give a case study, the failure probability (reliability) and the hit rate (performance) of the L1 cache in a LEON3 microprocessor are analyzed, while the microprocessor is running a set of benchmarks. Essential insights can be provided from our results to give better performance-reliability tradeoffs for cache designers.
Part of the book: Dependability Engineering
Multivariate adaptive regression splines (MARSP) is a nonparametric regression method. It is an adaptive procedure which does not have any predetermined regression model. With that said, the model structure of MARSP is constructed dynamically and adaptively according to the information derived from the data. Because of its ability to capture essential nonlinearities and interactions, MARSP is considered as a great fit for high-dimension problems. This chapter gives an application of MARSP in semiconductor field, more specifically, in standard cell characterization. The objective of standard cell characterization is to create a set of high-quality models of a standard cell library that accurately and efficiently capture cell behaviors. In this chapter, the MARSP method is employed to characterize the gate delay as a function of many parameters including process-voltage-temperature parameters. Due to its ability of capturing essential nonlinearities and interactions, MARSP method helps to achieve significant accuracy improvement.
Part of the book: Topics in Splines and Applications