Process Variation Aware Issue Queue Design

Deep-submicron designs have to take care of process variation effects as variations in critical process parameters result in large variations in access latencies of hardware components. This is severe in the case of memory components as minimum sized transistors are used in their design. In this work, by considering on-chip data caches, we study the effect of access latency variations on performance. We discuss performance losses due to the worst-case design, wherein the entire cache operates with the worst-case process variation delay, followed by process variation aware cache designs which work at set-level granularity. We then propose a technique called block rearrangement to minimize performance loss incurred by a process variation aware cache which works at set-level granularity. Using block rearrangement technique, we rearrange the physical locations of cache blocks such that a cache set can have its "n" blocks (assuming a n-way set-associative cache) in multiple rows instead of a single row as in the case of a cache with conventional addressing scheme. By distributing blocks of a cache set over multiple sets, we minimize the number of sets being affected by process variation. We evaluate our technique using SPEC2000 CPU benchmarks and show that our technique achieves significant performance benefits over caches with conventional addressing scheme

Winner-Take- All (WTA) circuit chooses a winner input from a set of input signals. WTA circuits employ transistors that operate in the sub-threshold or weak inversion region. With technology scaling, sub-threshold circuits are prone to mismatch and process variations which degrade the performance of these circuits. This paper presents a novel WTA circuit that employs a CMOS double pair transistor as a translinear element that is more tolerant to mismatch and process variations. Monte Carlo simulations are used to compare the performance of the existing and the proposed topologies to estimate the effects of process and mismatch variations. Results show that the proposed circuit is able to reduce the range of variations by three orders of magnitude compared to original one for similar variations in the transistor parameters. The proposed element also shows better accuracy in replicating the maximum input current compared to the original circuit.

Manufacturing-time process (P) variations and runtime voltage (V) and temperature (T) variations can affect a DRAM's performance severely. To counter these effects, DRAM vendors provide substantial design-time PVT timing margins to guarantee correct DRAM functionality under worst-case operating conditions. Unfortunately, with technology scaling these timing margins have become large and very pessimistic for a majority of the manufactured DRAMs. While run-time variations are specific to operating conditions and as a result, their margins difficult to optimize, process variations are manufacturing-time effects and excessive process-margins can be reduced at run-time, on a per-device basis, if properly identified. In this paper, we propose a generic post-manufacturing performance characterization methodology for DRAMs that identifies this excess in process-margins for any given DRAM device at runtime, while retaining the requisite margins for voltage (noise) and temperature variations. By doing so, the methodology ascertains the actual impact of process-variations on the particular DRAM device and optimizes its access latencies (timings), thereby improving its overall performance. We evaluate this methodology on 48 DDR3 devices (from 12 DIMMs) and verify the derived timings under worst-case operating conditions, showing up to 33.3% and 25.9% reduction in DRAM read and write latencies, respectively.

Analysis and verification environments for next-generation nano-scale RFIC designs must be able to cope with increasing design complexity and to account for new effects, such as process variations and Electromagnetic (EM) couplings. Designed-in passives, substrate, interconnect and devices can no longer be treated in isolation as the interactions between them are becoming more relevant to the behavior of the complete system. At the same time variations in process parameters lead to small changes in the device characteristics that may directly affect system performance. These two effects, however, cannot be treated separately as the process variations that modify the physical parameters of the devices also affect those same EM couplings. Accurately capturing the effects of process variations as well as the relevant EM coupling effects requires detailed models that become very expensive to simulate. Reduction techniques able to handle parametric descriptions of linear systems are necessary in order to obtain better simulation performance. In this work we discuss parametric Model Order Reduction techniques based on Structure-Preserving formulations that are able to exploit the hierarchical system representation of designed-in blocks, substrate and interconnect, in order to obtain more efficient simulation models.

The quality of a delay test set hinges not only on test patterns and the distribution of the delay defects but on the variations in process parameters as well. Process variations result in the same delay test set displaying differences from die to die in the detection of particular delay defects at the identical circuit node. The application of an identical test set to all devices independent of process variations consequently results in delivering inefficiencies in test time utilization. This paper proposes a delay test technique that adaptively changes the size of the test set based on the position of the device in the process variation space in order to maximize test quality within a given test time.

The concept of chip-to-chip information propagation by spoof surface plasmon polariton (SSPP) meta-surface at terahertz frequency has been introduced of late, that promises data transfer with high bandwidth, low crosstalk, and low energy consumption. As the exotic electromagnetic properties of the metasurface derive from its designed geometric pattern and periodicity, any possible variation of fabrication process parameters may affect the design pattern and consequently the information capacity of SSPP interconnects. In this work, we have investigated the extent of performance degradation of SSPP interconnect with the statistical variation of geometric pattern of the metasurface. We also described the technique of the design of appropriate analog circuit so that the loss of signal integrity incurred by the process variation can be recuperated in real time.

This paper investigates the transition frequencies () of an inductively terminated CMOS source follower buffer for negative resistance behavior at which the effective shunt resistance looking into the source of the buffer cell changes sign. Possible limiting frequencies of oscillation are determined based on resonators formed by a grounded gate inductor and a parasitic capacitance at the gate of the negative resistance buffer cell. The range of frequencies of oscillation of this negative resistance buffer cell for variations in the different circuit parameters/elements is explored. Following this, a millimeter wave (MMW) oscillator is simulated using the IBM 130 nm CMOS process technology which can operate at 70 GHz. High-frequency MOSFET model was used for these simulations. The cell had an extremely low power dissipation of under 3 mW. Extensive Monte Carlo simulations were carried out for manufacturability analysis considering up to 50% variation in process and geometrical parameters, supply voltage, and ambient temperature. Noise analysis and a simulated estimate of the phase noise in an MMW LC VCO application is also reported.

The inter-die and intra-die variations in process parameters (in particular, threshold voltage (Vt)) can lead to large number of failures in an SRAM array, thereby, degrading the design yield in nanometer technologies. To improve parametric yield of nano-scaled memories, different circuit and architectural level techniques can be used. In this paper, we first analyze and model different SRAM failures due to parameter variations, and discuss test methodologies to test for process variation induced failures. Next, we describe two different self-repairing techniques?at the circuit level, using adaptive body biasing and at the architecture level, using Built-in-self-test (BIST), redundancy and address remapping. The discussed self-repair mechanisms can improve design yield much beyond what can be achieved using row/column redundancy and error correcting codes (ECC) alone.

A generalized technique is proposed for modeling the effects of process variations on dynamic power by directly relating the variations in process parameters to variations in dynamic power of a digital circuit. The dynamic power of a 2-input NAND gate is characterized by mixed-mode simulations, to be used as a library element for 65nm gate length technology. The proposed methodology is demonstrated with a multiplier circuit built using the NAND gate library, by characterizing its dynamic power through Monte Carlo analysis. The statistical technique of response surface methodology (RSM) using design of experiments (DOE) and least squares method (LSM), are employed to generate a for gate power to account for simultaneous variations in multiple process parameters. We demonstrate that our hybrid model based statistical design approach results in considerable savings in the power budget of low power CMOS designs with an error of less than 1%, with significant reductions in uncertainty by at least 6X on a normalized basis, against worst case design

As process technologies continually advance, process variation has greatly increased and has gradually become one of the most critical factors for IC manufacturing. Furthermore, these increasingly complex processes continue to make greater use of stressors for mobility enhancement, thus requiring large volumes of data for extensive characterization of layout-dependent effects (LDE) for validation of both SPICE models and design for manufacturing. Transistor threshold voltage (Vt) is a commonly used parameter both for characterization during process development and for monitoring of volume manufacturing. To adequately quantify local process variation or LDE, Vt must be measured for a sufficiently large number of device-under-tests (DUTs) to obtain a statistically representative sample population. The number of Vt measurements required to obtain such a statistically significant result, however, requires extremely long testing time, especially for array-based test structure designs including thousands of DUTs. In this paper, we present a very fast threshold voltage measurement methodology using an operational amplifier-based source-measure unit test configuration, which greatly improves testing efficiency and accuracy, and is not sensitive to process variation. The proposed test methodology can improve Vt testing time by a factor of 5-10 relative to the commonly used binary-search algorithm, and by a factor of ~2 relative to an optimized interpolation algorithm, and achieves better accuracy (standard deviation of Vt = 0.15 mV, versus typical accuracy of ~ 0.5 mV for the two algorithms mentioned). Furthermore, the layout and configuration of conventional test structures need not be modified to adapt the proposed methodology. The measured results from the most advanced process technology nodes demonstrate the testing efficiency and accuracy of the proposed test structure in characterizing the large number of DUTs required for quantifying process variation or LDEs.

With continuous scaling of transistors in each technology generation, NBTI and Process Variation (PV) have become very important silicon reliability problems for the micro processor industry. In this paper, we develop an analytical model to capture the impact of NBTI in the presence of PV for use in architecture simulations. We capture the following aspects in the model: i) variation in NBTI related to stress and recovery due to workloads, ii) temporal variation in NBTI due to Random Charge Fluctuation (RCF) and iii) Random Dopant Fluctuation (RDF) due to process variation. We use this model to analyze the combined impact of NBTI and PV on a memory structure (register file) and a logic structure (Kogge-Stone adder). We show that the impact of the threshold voltage variations due to NBTI and PV over the nominal degradation can hurt the yield of the structures. Due to the combined effect of NBTI and PV across different benchmarks, 26 to 117 bits fail in a 8Kb size register file and the execution delay increases by 18% to 28% in a Kogge-Stone adder. We then discuss the implications of these results for architecture-level reliability techniques.

A series of plasma sprayed coatings of controlled microstructure was obtained by spraying three monosize sapphire powders using an axial injection torch in which the plasma gas composition and nozzle diameter were the only processing parameters varied. The effects of changes in these parameters on the coating splat morphology, porosity, angular crack distribution, and hardness are reported. The uniform, dense microstructure and the high hardness of 14 GPa (a level usually only associated with chromia thermal spray coatings) of the best alumina coatings resulted from using tightly controlled processing conditions and monodispersed precursor powders. The microstructural quality of plasma sprayed coatings and, hence, the coating properties can be improved significantly by minimizing variations in processing and raw material parameters.

During analog circuit synthesis in nanometer technology, process variability analysis is mandatory during design space exploration. This would ensure that the circuit will function as per specifications after fabrication even with impact of statistical variations in nanometer regimes. The methodology necessitates the evaluation of performance metrics of an analog circuit for different sizing instances of the transistors. Circuit simulation for performance evaluation is very time consuming and is seldom a choice while sizing a circuit for a chosen topology. The complexity of sizing methodology increases with the need to consider effects of variations in process and environment parameters. We employ macromodeling approach for analog circuits based on support vector machine (SVM), which enables efficient evaluation of performance of such circuits during sizing and yield optimization loops. The objective to improve evaluation efficiency has been the motivation behind efforts to develop performance macromodels, which should be as accurate as SPICE and at the same time have shorter evaluation time for use in the sizing of analog circuits, where they are used as substitutes for full circuit simulation during circuit sizing (synthesis). Process variability aware SVM macromodels are used in the multiobjective multivariate sizing method which is also yield optimal. Post design centering, the sized circuits will be able to provide functions as per specifications upon fabrication. Its application as process variability analysis tool is illustrated on two stage op amp and a voltage controlled oscillator using 90 nm BSIM4 models of transistors.

In aggressively scaled technologies, reliability concerns such as oxide breakdown have become a key issue. Dynamic reliability management (DRM) has been proposed as a mechanism to dynamically explore the trade-off between system performance and reliability margin. However, existing DRM methods are hampered by the fact that they do not accurately model spatial and temporal variations in process and temperature parameters which have a strong impact on chip reliability. In addition, they make the simplifying assumption that the future workloads are identical to the currently observed one. This makes them sensitive to sudden workload variations and outliers. In this paper, we present a novel workload-aware dynamic reliability management framework that accounts for local variations in both the process and temperature. The reliability estimation, along with the predicted remaining workload is fed to a dynamic voltage/frequency scaling module to manage the system reliability and optimize processor performance. Using a fast on-line analytical/table-look-up method we demonstrate an average error of 1% with up to 5 orders of magnitude speedup compared to Monte Carlo simulation. Experiments on an Alpha-like processor show our DRM framework fully utilizes the available margin and achieves 28.7% performance improvement on average.

Leakage power has already become the major contributor to the total on-chip power consumption, rendering its estimation a necessary step in the IC design flow. The problem is further exacerbated with the increasing uncertainty in the manufacturing process known as process variability. We develop a method to estimate the variation of leakage power in the presence of both intra-die and inter-die process variability. Various complicating issues of leakage prediction such as spatial correlation of process parameters, the effect of different input states of gates on the leakage, and DIBL and stack effects are taken into account while we model the simultaneous variability of the two most critical process parameters, threshold voltage and effective channel length. Our subthreshold leakage current model is shown to fit closely on the HSPICE Monte Carlo simulation data with an average coefficient of determination (R <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> ) value of 0.9984 for all the cells of a standard library. We also demonstrate the adjustability of this model to wider ranges of variation and its extendability to future technology scalings. We show that our framework imposes little timing penalty on the system design flow and is applicable to real design cases. The procedures explained in this paper are part of VAREX, an academic variability modeling framework for estimation of the effect of process variation on power consumption and performance of Multiprocessor SoCs.