We present a square root law for active sensing of phase θ of a single pixel using optical probes that pass through a single-mode lossy thermal-noise bosonic channel. Specifically, we show that, when the sensor uses an n-mode covert optical probe, the mean squared error (MSE) of the resulting estimator θnscales as 〈(θ-θ̂n)2〉 = O(1/√n) improving the scaling necessarily leads to detection by the adversary with high probability. We fully characterize this limit and show that it is achievable using laser light illumination and a heterodyne receiver, even when the adversary captures every photon that does not return to the sensor and performs arbitrarily complex measurement as permitted by the laws of quantum mechanics.

Mobile devices spend most of the time in standby mode. Supported features and functionalities are increasing in each newer model. With the wide spread adaptation of multitasking in mobile devices, retaining current status and data for all active tasks is critical for user satisfaction. Extending battery life in portable mobile devices necessitates the use of minimum possible energy in standby mode while retaining present states for all active tasks. This paper for the first time, explains the low voltage data-retention failure mechanism in flops. It analyzes the impact of design and process parameters on the data retention failure. Statistical nature of data retention failure is established and validated with extensive Monte-Carlo simulations across various process corners. Finally, silicon measurement from several 28nm industrial mobile chips is presented showing good correlation of retention failure prediction from simulation.

This paper discusses a novel clock gating cell (CGC) optimized for low-power and low-voltage operation. First, the limitations of the conventional CGC topology are analyzed and several improvements are proposed. Next, the new CGC topology is introduced and compared to the conventional one in terms of dynamic clock power, leakage, area, timing, and low-voltage operation. Finally, the paper discusses the silicon measurements taken to verify the correct functionality of the new circuit in a 45-nm technology optimized for low standby power.

In this paper, a profit-aware design metric is proposed to consider the overall merit of a design in terms of power and performance. A statistical design methodology is then developed to improve the economic merit of a design considering frequency binning and product price profile. A low-complexity sensitivity-based gate sizing algorithm is developed to improve economic gain of a design over its initial yield-optimized design. Finally, we present an integrated design methodology for simultaneous sizing and bin boundary determination to enhance profit under an area constraint. Experiments on a set of ISCAS'85 benchmarks show in average 19% improvement in profit for simultaneous sizing and bin boundary determination, considering both leakage power dissipation and delay bounds compared to a design initially optimized for 90% yield at iso-area in 70-nm bulk CMOS technology.

Independent control of front and back gate in double gate (DG) devices can be used to merge parallel transistors in noncritical paths. This reduces the effective switching capacitance and, hence, the dynamic power dissipation of a circuit. However, efficient design of large-scale circuits with DG devices is not well explored due to lack of proper modeling and large-scale design simulation tools. In this paper, we propose several low-power circuit options using independent gate FinFETs. We developed semianalytical models for different FinFET logic gates to predict their performance. An efficient circuit synthesis methodology comprised of proposed low-power logic options in FinFET design library has been developed. Results show about 8.5% area savings and 18% power savings over conventional FinFET technology for ISCAS85 benchmark circuits in 45-nm technology with no performance penalty.

Designing high-performance systems with high yield under parameter variations has raised serious design challenges in nanometer technologies. In this paper, we propose a profit-aware yield model, based on which we present a statistical design methodology to improve profit of a design considering frequency binning and product price profile. A low-complexity sensitivity-based gate sizing algorithm is developed to improve the profitability of design over an initial yield-optimized design. We also propose an algorithm to determine optimal bin boundaries for maximizing profit with frequency binning. Finally, we present an integrated design methodology for simultaneous sizing and bin placement to enhance profit under an area constraint. Experiments on a set of ISCAS85 benchmarks show up to 26% (36%) improvement in profit for fixed bin (for simultaneous sizing and bin placement) with three frequency bins considering both leakage and delay bounds compared to a design optimized for 90% yield at iso-area.

Under inter-die and intra-die parameter variations, the delay of a pipelined circuit follows a statistical distribution. This paper presents analytical models to estimate yield for a pipelined design based on delay distributions of individual pipe stages. Using the proposed models, it is shown that a change in logic depth and an imbalance between stage yields can improve the design yield and the area of a pipeline a circuit. A novel statistical methodology is developed to enhance yield of a pipelined circuit under an area constraint. Based on the concept of area borrowing, the results show that incorporating a proper imbalance among stage areas in a four-stage pipeline improves design yield up to 15.4% for the same area (and reduces area up to 8.4% under a yield constraint) compared with a balanced design

Under inter- and intra-die parameter variations, delay of a pipelined circuit follows a statistical distribution. Hence, a pipelined circuit suffers yield loss with respect to violation of target delay constraint unless an overly pessimistic worst-case design approach is followed. We propose a statistical approach for pipeline design to enhance yield with respect to a target delay under an area budget. Right choice of the number of pipeline stages to enhance yield under an area constraint is addressed using simple statistical yield models. Next, individual stages are designed for maximizing yield under area constraint for the stages. Once the independently optimized stages are combined to form a pipeline, we propose a final global optimization step to improve pipeline yield with no area overhead, based on a concept of area borrowing. Optimization results show that, the proposed statistical design approach for pipeline improves the overall yield up to 12% over conventional design for equal area.

Operating frequency of a pipelined circuit is determined by the of the slowest pipeline stage. However, under statistical delay variation in sub-100 nm technology regime, the slowest stage is not readily identifiable and the estimation of the pipeline yield with respect to a target delay is a challenging problem. We have proposed analytical models to estimate yield for a pipelined design based on delay distributions of individual pipe stages. Using the proposed models, we have shown that change in logic depth and imbalance between the stage delays can improve the yield of a pipeline. A statistical methodology has been developed to optimally design a pipeline circuit for enhancing yield. Optimization results show that, proper imbalance among the stage delays in a pipeline improves design yield by 9% for the same area and performance (and area reduction by about 8.4% under a yield constraint) over a balanced design.

In nanoscale technology, large variations in process parameters produce wide delay spread in high performance circuit. In this paper the authors developed analytical models for yield prediction with respect to delay variation of pipeline design. The converse problem of estimating the design space for individual pipe stages based on a target yield has been addressed. For an example 4 stage pipelined circuit proposed analytical models are verified to predict yield within 2% of results obtained from Monte-Carlo Hspice simulation

Reducing the energy consumption of a real-time system has emerged as an important design concern. In this paper, we propose GAARP, an adaptive scalable architecture targeted toward algorithm-specific tasks for just-in-time performance using the right amount of power. The architecture consists of Globally Asynchronous and Locally Synchronous (GALS) building blocks, where the processing hardware is realized by a set of smaller slices of similar structure, each running synchronously with independent clocks. We demonstrate that, for different real-time commercial applications with algorithm-specific jobs like online transaction processing, digital filtering, Fourier transform, etc., the proposed architecture allows dynamic load-balancing and adaptive intertask voltage scaling based on the load in each of the processing units. Compared to a synchronous implementation of the same functionality, we show that the proposed hardware can achieve higher efficiency in terms of power and performance by exploiting the flexibility to balance the load and change the supply voltage. The architecture also lends itself to process tolerance since it can detect process-shifts for the individual processing units and determine the appropriate operating voltage/frequency for each unit. Simulation results for two representative applications show that, for a modest system configuration and random job distribution, we obtain up to 67 percent improvement in MOPS/W (millions of operations per second per watt) over a fully synchronous implementation.

Process parameter variations are expected to be significantly high in a sub-50-nm technology regime, which can severely affect the yield, unless very conservative design techniques are employed. The parameter variations are random in nature and are expected to be more pronounced in minimum geometry transistors commonly used in memories such as SRAM. Consequently, a large number of cells in a memory are expected to be faulty due to variations in different process parameters. We analyze the impact of process variation on the different failure mechanisms in SRAM cells. We also propose a process-tolerant cache architecture suitable for high-performance memory. This technique dynamically detects and replaces faulty cells by dynamically resizing the cache. It surpasses all the contemporary fault tolerant schemes such as row/column redundancy and error-correcting code (ECC) in handling failures due to process variation. Experimental results on a 64-K direct map L1 cache show that the proposed technique can achieve 94% yield compared to its original 33% yield (standard cache) in a 45-nm predictive technology under /spl sigma//sub Vt-inter/=/spl sigma//sub Vt-intra/=30 mV.