With the advent of the slowdown in DRAM capacitor scaling [1] and the increased reliability problems of traditional 6T SRAM memories [2], industry and academia have looked for alternative memory cells. Among those, gain-cells have attracted significant attention due to their smaller size (compared to SRAM) and non-destructive read operation (compared to DRAM) as well as considerable low power and reasonable robustness. This paper first summarizes the available evidences of SRAM and eDRAM in commercial and test chips. Then, it analyzes the performance, reliability and scaling of eDRAM gain-cells in 10 and 7 nm FinFET technology; as well as above and below VT(i.e. sub-threshold).

Bio-medical wearable devices restricted to their small-capacity embedded-battery require energy-efficiency of the highest order. However, minimum-energy point (MEP) at sub-threshold voltages is unattainable with SRAM memory, which fails to hold below 0.3V because of its vanishing noise margins. This paper examines minimum-energy operation of 2T and 3T1D e-DRAM gain cells as an alternative to SRAM at 32nm technology node with different design points: up-sizing transistors, using high-Vth transistors, read/write wordline assists and temperature. First, the e-DRAM cells are evaluated without considering any process variations. The design-space is explored by creating a kriging meta-model to reduce the number of simulations. Finally, a full-factorial statistical analysis of e-DRAM cells is performed in presence of threshold voltage variations. The effect on mean MEP is also reported.

In this paper, we analyze the suitability of implementing embedded DRAM (eDRAM) cells on FinFET technology compared to classical planar MOSFETs. The results show a significant improvement in overall cell performance for multi-gate devices. While pFinFET-based memories showed better cell behavior and variability robustness, mixed n/pFinFET cells had the highest working frequency and a negligible impact on degradation. Finally, we show that a multiple fin-height strategy can be used to reduce the layout area of the eDRAM cells (>10%).

Nanoscale circuits are subject to a wide range of new limiting phenomena making essential to investigate new design strategies at the circuit and architecture level to improve its performance and reliability. Proactive reconfiguration is an emerging technique oriented to extend the system lifetime of memories affected by aging. In this brief, we present a new approach for static random access memory (SRAM) design that extends the cache lifetime when considering process variation and aging in the memory cells using an adaptive strategy. To track the aging in the SRAM cells we propose an on-chip monitoring technique. Our results show the technique as a feasible way to extend the cache lifetime up to 5X.

Scaling of device dimensions toward nano-scale regime has made it essential to innovate novel design techniques for improving the circuit robustness. This work proposes an implementation of adaptive proactive reconfiguration methodology that can first monitor process variability and BTI aging among 6T SRAM memory cells and then apply a recovery mechanism to extend the SRAM lifetime. Our proposed technique can extend the memory lifetime between 2X to 4.5X times with a silicon area overhead of around 10% for the monitoring units, in a 1kB 6T SRAM memory chip.

Due to increased leakage currents and variability, classical bulk technology is reaching its scaling limits and some alternatives must be found. FinFETs are one of those alternatives. Through their 3D structure, they achieve better channel control which is the key to scalability. However, some sources of variability still remain. The impact of this technology shift on SRAM and DRAM memories is analyzed in this work.

Process variations and device aging have a significant impact on the reliability and performance of nano scale integrated circuits. Proactive reconfiguration is an emerging technique to extend the lifetime of embedded SRAM memories. This work introduces a novel version that modifies and enhances the advantages of this method by considering the process variability impact on the memory components. Our results show between 30% and 45% SRAM lifetime increases over the existing proactive reconfiguration technique and between 1.7X and ~10X improvement over the non-proactive reconfiguration.