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Short-loop process monitoring structures (usually simple device I-V, C-V measurements made after M1 fabrication) are commonly put in wafer scribelines. These test structures are almost always design independent and measured or monitored by the foundry to keep track of process deviations. We propose a design-dependent process monitoring strategy that can accurately predict design performance based on Ieff-based delay and Ioff-based leakage power estimates. Further, we use the predicted delay and power for early yield estimation to: 1) prune bad wafers to save test and back-end manufacturing costs, and 2) prune bad dies to save testing costs. Combining chip pruning with wafer pruning, we can reduce the cost per good chip by up to 13%. Such design-dependent process monitoring can help reduce process optimization effort, and enable quicker yield ramp besides saving testing and manufacturing costs. [ABSTRACT FROM PUBLISHER]

The most challenging problem in the current block-based statistical static timing analysis (SSTA) is how to handle the max operation efficiently and accurately. Existing SSTA techniques suffer from limited modeling capability by using a linear delay model with Gaussian distribution, or have scalability problems due to expensive operations involved to handle non-Gaussian variation sources or nonlinear delays. To overcome these limitations, we propose efficient algorithms to handle the max operation in SSTA with both quadratic delay dependency and non-Gaussian variation sources simultaneously. Based on such algorithms, we develop an SSTA flow with quadratic delay model and non-Gaussian variation sources. All the atomic operations, max and add, are calculated efficiently via either closed-form formulas or low dimension (at most 2-D) lookup tables. We prove that the complexity of our algorithm is linear in both variation sources and circuit sizes, hence our algorithm scales well for large designs. Compared to Monte Carlo simulation for non-Gaussian variation sources and nonlinear delay models, our approach predicts the mean, standard deviation and 95% percentile point with less than 2% error, and the skewness with less than 10% error. [ABSTRACT FROM PUBLISHER]

Modeling spatial variation is important for statistical analysis. Most existing works model spatial variation as spatially correlated random variables. We discuss process origins of spatial variability, all of which indicate that spatial variation comes from deterministic across-wafer variation, and purely random spatial variation is not significant. We analytically study the impact of across-wafer variation and show how it gives an appearance of correlation. We have developed a new die-level variation model considering deterministic across-wafer variation and derived the range of conditions under which ignoring spatial variation altogether may be acceptable. Experimental results show that for statistical timing and leakage analysis, our model is within 2% and 5% error from exact simulation result, respectively, while the error of the existing distance-based spatial variation model is up to 6.5% and 17%, respectively. Moreover, our new model is also 6\times faster than the spatial variation model for statistical timing analysis and 7\times faster for statistical leakage analysis. [ABSTRACT FROM PUBLISHER]

Interconnect congestion estimation plays an important role in the physical design of integrated circuits. Fast congestion analysis prior to global routing enhances the placement quality and improves the routability for the subsequent routing phases. This article presents a novel congestion estimation method for a wire layout with bounded detours and bends. Experimental results on benchmarks demonstrate the efficiency and accuracy of our approach. [ABSTRACT FROM AUTHOR]

With the increase of the complexity of circuits, fast estimation can provide some vital information for optimal layout decisions. Fast congestion prediction plays an important role in the physical layout of VLSI design. In this paper, we present a probabilistic estimation approach with via minimization constraints. Our model is more realistic than previous models. It has more flexibility for wires to have more usage area to bypass congested regions and blockages. The experiment on routing benchmarks demonstrates the effectiveness of our approach. [ABSTRACT FROM AUTHOR]

For nanometer manufacturing, process variation causes significant uncertainty for circuit performance verification. Statistical static timing analysis (SSTA) is thus developed to estimate timing distribution under process variation. Most existing SSTA techniques have difficulty in handling the non-Gaussian variation distribution and nonlinear dependence of delay on variation sources. To address this problem, we first propose a new method to approximate the max operation of two non-Gaussian random variables through second-order polynomial fitting. With such approximation, we then present new non-Gaussian SSTA algorithms for three delay models: quadratic model, quadratic model without crossing terms (semiquadratic model), and linear model. All the atomic operations (max and sum) of our algorithms are performed by closed-form formulas; hence, they scale well for large designs. Experimental results show that compared to the Monte Carlo simulation, our approach predicts the mean, standard deviation, skewness, and 95% percentile point within 1%, 1%, 6%, and 1% error, respectively. [ABSTRACT FROM AUTHOR]

Interconnect planning is an important issue in VLSI designs. Routing congestion is an important problem during placement. This paper presents a novel approach to estimating the routing congestion on the hexagonal model with bounded-length detours. It is the first probabilistic estimation work on hexagonal topologies. A combinatorial estimation algorithm is presented. The experimental results demonstrate the effectiveness of the method. [ABSTRACT FROM AUTHOR]

Today's very deep submicron IC technology enables high-performance analog and digital applications to be integrated on a single piece of silicon. For this effort, a design of 2.5 giga sample per second (GSPS) receiver-on-a-chip (ROC) is presented. For our design, we take advantage of a compensation technique to reduce spurs and improve instantaneous dynamic range. A major goal is to produce a low-cost, small, and lightweight, and low-power ROC. Our design will cover a 1-GHz bandwidth (125-1125 MHz), and it will correctly process two simultaneous signals by detecting their frequency, pulsewidth (PW), and time of arrival (TOA). The single or dual signal, spur-free dynamic ranges and two signal instantaneous dynamic ranges of our design are high. The minimum frequency separation of two signals is 10 MHz (one channel width), and the maximum amplitude separation (dynamic range) of two signals is 18 dB with the second signal false alarm less than 1%. [ABSTRACT FROM AUTHOR]

Three dimensional (3-D) routing is an important step in deep submicrometer very large-scale integrated design. Given a 3-D grid graph and a set of two-terminal nets to be routed, we propose a probabilistic model to calculate the routing density (congestion) on each edge of the grid graph. The routing density provides a direct congestion estimation. Our experimental results demonstrate the effectiveness of the method on routing benchmarks. [ABSTRACT FROM AUTHOR]