Effectiveness and scaling trends of leakage control techniques for sub-130nm CMOS technologies

This paper compares the effectiveness of different leakage control techniques in deep submicron (DSM) bulk CMOS technologies. Simulations show that the 3-5x increase in IOFF/µm per generation is offsetting the savings in switching energy obtained from technology scaling. We compare both the transistor IOFF reduction and ION degradation due to each technique for the 130nm-70nm technologies. Our results indicate that the effectiveness of leakage control techniques and the associated energy vs. delay tradeoffs depend on the ratio of switching to leakage energies for a given technology. We use our findings to design a 70nm low power word line driver scheme for a 256 entry, 64-bit register file (RF). As a result, the leakage (total) energy of the word line drivers is reduced by 3x(2.5x) and for the RF by up to 35%(25%) respectively

A Study of Nanometer Semiconductor Scaling Effects on Microelectronics Reliability

The desire to assess the reliability of emerging scaled microelectronics technologies through faster reliability trials and more accurate acceleration models is the precursor for further research and experimentation in this relevant field. The effect of semiconductor scaling on microelectronics product reliability is an important aspect to the high reliability application user. From the perspective of a customer or user, who in many cases must deal with very limited, if any, manufacturer's reliability data to assess the product for a highly-reliable application, product-level testing is critical in the characterization and reliability assessment of advanced nanometer semiconductor scaling effects on microelectronics reliability. This dissertation provides a methodology on how to accomplish this and provides techniques for deriving the expected product-level reliability on commercial memory products. Competing mechanism theory and the multiple failure mechanism model are applied to two separate experiments; scaled SRAM and SDRAM products. Accelerated stress testing at multiple conditions is applied at the product level of several scaled memory products to assess the performance degradation and product reliability. Acceleration models are derived for each case. For several scaled SDRAM products, retention time degradation is studied and two distinct soft error populations are observed with each technology generation: early breakdown, characterized by randomly distributed weak bits with Weibull slope Beta=1, and a main population breakdown with an increasing failure rate. Retention time soft error rates are calculated and a multiple failure mechanism acceleration model with parameters is derived for each technology. Defect densities are calculated and reflect a decreasing trend in the percentage of random defective bits for each successive product generation. A normalized soft error failure rate of the memory data retention time in FIT/Gb and FIT/cm2 for several scaled SDRAM generations is presented revealing a power relationship. General models describing the soft error rates across scaled product generations are presented. The analysis methodology may be applied to other scaled microelectronic products and key parameters

Modeling and Mitigation of Soft Errors in Nanoscale SRAMs

Energetic particle (alpha particle, cosmic neutron, etc.) induced single event data upset or soft error has emerged as a key reliability concern in SRAMs in sub-100 nanometre technologies. Low operating voltage, small node capacitance, high packing density, and lack of error masking mechanisms are primarily responsible for the soft error susceptibility of SRAMs. In addition, since SRAM occupies the majority of die area in system-on-chips (SoCs) and microprocessors, different leakage reduction techniques, such as, supply voltage reduction, gated grounding, etc., are applied to SRAMs in order to limit the overall chip leakage. These leakage reduction techniques exponentially increase the soft error rate in SRAMs. The soft error rate is further accentuated by process variations, which are prominent in scaled-down technologies. In this research, we address these concerns and propose techniques to characterize and mitigate soft errors in nanoscale SRAMs. We develop a comprehensive analytical model of the critical charge, which is a key to assessing the soft error susceptibility of SRAMs. The model is based on the dynamic behaviour of the cell and a simple decoupling technique for the non-linearly coupled storage nodes. The model describes the critical charge in terms of NMOS and PMOS transistor parameters, cell supply voltage, and noise current parameters. Consequently, it enables characterizing the spread of critical charge due to process induced variations in these parameters and to manufacturing defects, such as, resistive contacts or vias. In addition, the model can estimate the improvement in critical charge when MIM capacitors are added to the cell in order to improve the soft error robustness. The model is validated by SPICE simulations (90nm CMOS) and radiation test. The critical charge calculated by the model is in good agreement with SPICE simulations with a maximum discrepancy of less than 5%. The soft error rate estimated by the model for low voltage (sub 0.8 V) operations is within 10% of the soft error rate measured in the radiation test. Therefore, the model can serve as a reliable alternative to time consuming SPICE simulations for optimizing the critical charge and hence the soft error rate at the design stage. In order to limit the soft error rate further, we propose an area-efficient multiword based error correction code (MECC) scheme. The MECC scheme combines four 32 bit data words to form a composite 128 bit ECC word and uses an optimized 4-input transmission-gate XOR logic. Thus MECC significantly reduces the area overhead for check-bit storage and the delay penalty for error correction. In addition, MECC interleaves two composite words in a row for limiting cosmic neutron induced multi-bit errors. The ground potentials of the composite words are controlled to minimize leakage power without compromising the read data stability. However, use of composite words involves a unique write operation where one data word is written while other three data words are read to update the check-bits. A power efficient word line signaling technique is developed to facilitate the write operation. A 64 kb SRAM macro with MECC is designed and fabricated in a commercial 90nm CMOS technology. Measurement results show that the SRAM consumes 534 μW at 100 MHz with a data latency of 3.3 ns for a single bit error correction. This translates into 82% per-bit energy saving and 8x speed improvement over recently reported multiword ECC schemes. Accelerated neutron radiation test carried out at TRIUMF in Vancouver confirms that the proposed MECC scheme can correct up to 85% of soft errors

Near-optimal precharging in high-performance nanoscale CMOS caches

High-performance caches statically pull up the bit-lines in all cache subarrays to optimize cache access latency. Unfortunately, such architecture results in a significant waste of energy in nanoscale CMOS implementations due to high leakage and bitline discharge in the unaccessed subarrays. Recent research advocates bitline isolation to control precharging of individual subarrays using bitline precharge devices. In this paper, we carefully evaluate the energy and performance trade-offs of bitline isolation, and propose a technique to exploit nearly its full potential to eliminate discharge and reduce overall energy in level-one caches. Cycle-accurate and circuit simulation results of a wide-issue superscalar processor indicate that: 1) in future CMOS technologies (e.g., 70 nm and beyond), cache architectures that exploit bitline isolation can eliminate up to 90% of the bitline discharge; 2) on- demand precharging (i.e., decoding the address and subsequently precharging the accessed subarrays) is not viable in level-one caches because precharging increases the cache access latency; and 3) our proposal for gated precharging to exploit subarray reference locality and precharging only the recently accessed subarrays eliminates nearly all of bitline discharge in nanoscale CMOS caches with only a 1% of performance degradatio

Design and Analysis of Low-power SRAMs

The explosive growth of battery operated devices has made low-power design a priority in recent years. Moreover, embedded SRAM units have become an important block in modern SoCs. The increasing number of transistor count in the SRAM units and the surging leakage current of the MOS transistors in the scaled technologies have made the SRAM unit a power hungry block from both dynamic and static perspectives. Owing to high bitline voltage swing during write operation, the write power consumption is dominated the dynamic power consumption. The static power consumption is mainly due to the leakage current associated with the SRAM cells distributed in the array. Moreover, as supply voltage decreases to tackle the power consumption, the data stability of the SRAM cells have become a major concern in recent years. To reduce the write power consumption, several schemes such as row based sense amplifying cell (SAC) and hierarchical bitline sense amplification (HBLSA) have been proposed. However, these schemes impose architectural limitations on the design in terms of the number of words on a row. Beside, the effectiveness of these methods is limited to the dynamic power consumption. Conventionally, reduction of the cell supply voltage and exploiting the body effect has been suggested to reduce the cell leakage current. However, variation of the supply voltage of the cell associates with a higher dynamic power consumption and reduced cell data stability. Conventionally qualified by Static Noise Margin (SNM), the ability of the cell to retain the data is reduced under a lower supply voltage conditions. In this thesis, we revisit the concept of data stability from the dynamic perspective. A new criteria for the data stability of the SRAM cell is defined. The new criteria suggests that the access time and non-access time (recovery time) of the cell can influence the data stability in a SRAM cell. The speed vs. stability trade-off opens new opportunities for aggressive power reduction for low-power applications. Experimental results of a test chip implemented in a 130 nm CMOS technology confirmed the concept and opened a ground for introduction of a new operational mode for the SRAM cells. We introduced a new architecture; Segmented Virtual Grounding (SVGND) to reduce the dynamic and static power reduction in SRAM units at the same time. Thanks to the new concept for the data stability in SRAM cells, we introduced the new operational mode of Accessed Retention Mode (AR-Mode) to the SRAM cell. In this mode, the accessed SRAM cell can retain the data, however, it does not discharge the bitline. The new architecture outperforms the recently reported low-power schemes in terms of dynamic power consumption, thanks to the exclusive discharge of the bitline and the cell virtual ground. In addition, the architecture reduces the leakage current significantly since it uses the back body biasing in both load and drive transistors. A 40Kb SRAM unit based on SVGND architecture is implemented in a 130 nm CMOS technology. Experimental results exhibit a remarkable static and dynamic power reduction compared to the conventional and previously reported low-power schemes as expect from the simulation results