Single-Supply 3T Gain-Cell for Low-Voltage Low-Power Applications

Logic compatible gain cell (GC) embedded DRAM (eDRAM) arrays are considered an alternative to SRAM due to their small size, non-ratioed operation, low static leakage, and 2-port functionality. However, traditional GC-eDRAM implementations require boosted control signals in order to write full voltage levels to the cell to reduce the refresh rate and shorten access times. These boosted levels require either an extra power supply or on-chip charge pumps, as well as non-trivial level shifting and toleration of high voltage levels. In this paper, we present a novel, logic compatible, 3T GC-eDRAM bitcell that operates with a single supply voltage and provides superior write capability to conventional GC structures. The proposed circuit is demonstrated with a 2kb memory macro that was designed and fabricated in a mature 0.18um CMOS process, targeted at low-power, energy-efficient applications. The test array is powered with a single supply of 900mV, showing an 0.8ms worst-case retention time, a 1.3ns write-access time, and 2.4pW/bit of retention power. The proposed topology provides a bitcell area reduction of 43%, as compared to a redrawn 6T SRAM in the same technology, and an overall macro area reduction of 67% including peripherals

Design techniques for dense embedded memory in advanced CMOS technologies

University of Minnesota Ph.D. dissertation. February 2012. Major: Electrical Engineering. Advisor: Chris H. Kim. 1 computer file (PDF); viii, 116 pages.On-die cache memory is a key component in advanced processors since it can boost micro-architectural level performance at a moderate power penalty. Demand for denser memories only going to increase as the number of cores in a microprocessor goes up with technology scaling. A commensurate increase in the amount of cache memory is needed to fully utilize the larger and more powerful processing units. 6T SRAMs have been the embedded memory of choice for modern microprocessors due to their logic compatibility, high speed, and refresh-free operation. However, the relatively large cell size and conflicting requirements for read and write make aggressive scaling of 6T SRAMs challenging in sub-22 nm. In this dissertation, circuit techniques and simulation methodologies are presented to demonstrate the potential of alternative options such as gain cell eDRAMs and spin-torque-transfer magnetic RAMs (STT-MRAMs) for high density embedded memories.Three unique test chip designs are presented to enhance the retention time and access speed of gain cell eDRAMs. Proposed bit-cells utilize preferential boostings, beneficial couplings, and aggregated cell leakages for expanding signal window between data `1' and `0'. The design space of power-delay product can be further enhanced with various assist schemes that harness the innate properties of gain cell eDRAMs. Experimental results from the test chips demonstrate that the proposed gain cell eDRAMs achieve overall faster system performances and lower static power dissipations than SRAMs in a generic 65 nm low-power (LP) CMOS process. A magnetic tunnel junction (MTJ) scaling scenario and an efficient HSPICE simulation methodology are proposed for exploring the scalability of STT-MRAMs under variation effects from 65 nm to 8 nm. A constant JC0*RA/VDD scaling method is adopted to achieve optimal read and write performances of STT-MRAMs and thermal stabilities for a 10 year retention are achieved by adjusting free layer thicknesses as well as projecting crystalline anisotropy improvements. Studies based on the proposed methodology show that in-plane STT-MRAM will outperform SRAM from 15 nm node, while its perpendicular counterpart requires further innovations in MTJ material properties in order to overcome the poor write performance from 22 nm node

ULTRA ENERGY-EFFICIENT SUB-/NEAR-THRESHOLD COMPUTING: PLATFORM AND METHODOLOGY

Ph.DDOCTOR OF PHILOSOPH

Technology Implications for Large Last-Level Caches

Large last-level cache (L3C) is efficient for bridging the performance and power gap between processor and memory. Several memory technologies, including SRAM, STT-RAM (MRAM), and embedded DRAM (eDRAM), have been used or considered as the technology to implement L3Cs. However, each of them has inherent weaknesses: SRAM is relatively low density and dissipates high leakage; STT-RAM has long write latency and requires high write energy; eDRAM requires refresh. As future processors are expected to have larger last-level caches, the objective of this dissertation is to study the tradeoffs associated with using each of these technologies to implement L3Cs. In order to make useful comparisons between L3Cs built with SRAM, STT-RAM, and eDRAM, we consider and implement several levels of details. First, to obtain unbiased cache performance and power properties (i.e., read/write access latency, read/write access energy, leakage power, refresh power, area), we prototype caches based on realistic memory and device models. Second, we present simplistic analytical models that enable us to quickly examine different memory technologies under various scenarios. Third, we review power-optimization techniques for each of the technologies, and propose using a low-cost dead-line prediction scheme for eDRAM-based L3Cs to eliminate unnecessary refreshes. Finally, the highlight of this dissertation is the comparison and analysis of low-leakage SRAM, low write-energy STT-RAM, and refresh-optimized eDRAM. We report system performance, last-level cache energy breakdown, and memory hierarchy energy breakdown, using an augmented full-system simulator with the execution of a range of workloads and input sets. From the insights gained through simulation results, STT-RAM has the highest potential to save energy in future L3C designs. For contemporary processors, SRAM-based L3C results in the fastest system performance, whereas eDRAM consumes the lowest energy