Peter A. Habitz

Process and environmental variation impacts on ASIC timing

With each semiconductor process node, the impacts on performance of environmental and semiconductor process variations become a larger portion of the cycle time of the product. Simple guard-banding for these effects leads to increased product development times and uncompetitive products. In addition, traditional static timing methodologies are unable to cope with the large number of permutations of process, voltage, and temperature corners created by these independent sources of variation. In this paper we will discuss the sources of variation; by introducing the concepts of systematic inter-die variation, systematic intra-die variation and intra-die random variation. We will show that by treating these forms of variations differently, we can achieve design closure with less guard-banding than traditional methods.

Variation-aware performance verification using at-speed structural test and statistical timing

Meeting the tight performance specifications mandated by the customer is critical for contract manufactured ASICs. To address this, at speed test has been employed to detect subtle delay failures in manufacturing. However, the increasing process spread in advanced nanometer ASICs poses considerable challenges to predicting hardware performance from timing models. Performance verification in the presence of process variation is difficult because the critical path is no longer unique. Different paths become frequency limiting in different process corners. In this paper, we present a novel variation-aware method based on statistical timing to select critical paths for structural test. Node criticalities are computed to determine the probabilities of different circuit nodes being on the critical path across process variation. Moreover, path delays are projected into different process corners using their linear delay function forms. Experimental results for three multimillion gate ASICs demonstrate the effectiveness of our methods.

Modeling of thermal behavior in SOI structures

Several physics-based analytical steady-state heat flow models for silicon-on-insulator (SOI) devices are presented, offering approaches at different levels of accuracy and efficiency for prediction of temperature profiles induced by power dissipated in SOI MOSFETs. The approaches are verified with the rigorous device simulation based on the energy transport model coupled with the heat flow equation. The models describe the one-dimensional temperature profile in the silicon film of SOI structure and two-dimensional heat flow in FOX, accounting for heat loss to the substrate via BOX and FOX, heat loss to (or gain from) interconnects, and heat exchanges between devices. These models are applied to investigate thermal behavior in single SOI devices and two-device SOI structures.

Optimal margin computation for at-speed test

In the face of increased process variations, at-speed manufacturing test is necessary to detect subtle delay defects. This procedure necessarily tests chips at a slightly higher speed than the target frequency required in the field. The additional performance required on the tester is called test margin. There are many good reasons for margin including voltage and temperature requirements, incomplete test coverage, aging effects, coupling effects and accounting for modeling inaccuracies. By taking advantage of statistical timing, this paper proposes an optimal method of test margin determination to maximize yield while staying within a prescribed shipped product quality loss (SPQL) limit. If process information is available from wafer testing of scribe line structures or on-chip process monitoring circuitry, this information can be leveraged to determine a per- chip test margin which can further improve yield.

Statistical Evaluation of Electromigration Reliability at Chip Level

Chip level electromigration (EM) reliability is determined by: 1) the element level EM failure probability used for design guideline generation; and 2) the distribution of EM elements against design limits. Balancing these two factors is critical for a chip design to achieve the best performance while maintaining chip level EM reliability. This paper discusses the relationship between element level and chip level EM failure probability and provides examples of EM evaluation of chip designs.

Optimal Test Margin Computation for At-Speed Structural Test

In the face of increased process variations, at-speed manufacturing test is necessary to detect subtle delay defects. This procedure necessarily tests chips at a slightly higher speed than the target frequency required in the field. The additional performance required on the tester is called test margin . There are many good reasons for margin, including voltage and temperature requirements, incomplete test coverage, aging effects, coupling effects, and accounting for modeling inaccuracies. By taking advantage of statistical timing, this paper proposes an optimal method of test margin determination to maximize yield while staying within a prescribed shipped product quality loss limit. If process information is available from the wafer testing of scribe-line structures or on-chip process monitoring circuitry, this information can be leveraged to determine a per-chip test margin which can further improve yield.

A dynamic method for efficient random mismatch characterization of standard cells

To enable statistical static timing analysis, for each cell in a digital library, a timing model that considers variations must be characterized. In this paper, we propose a dynamic method to accurately and efficiently characterize a cells delay and output slew as a function of random mismatch variations. Based on a tight error bound for characterization using partial devices, our method sequentially performs simulations based on decreasing importance of devices and stops when the error requirement is met. Results on an industrial 32nm library demonstrate that the proposed method achieves significantly better accuracy-efficiency trade-off compared to other partial finite differencing approaches.

A Novel Method for Reducing Metal Variation With Statistical Static Timing Analysis

Process variation continues to increase with new technologies. With the advent of statistical static timing analysis (SSTA), multiple independent sources of variation can be modeled. This paper proposes a novel technique to reduce variability of metal process variation in SSTA. This novel method maximizes sensitivity cancellation to minimize variability. The developed methodology is simulated with SSTA in 65-nm technology and shows a reduction in variability.

Thermal simulation for SOI devices using thermal-circuit models and device simulation

Abstract A methodology combining a thermal-circuit model and device simulation is presented for SOI device simulation including self-heating. The efficiency and accuracy were verified against the rigorous device simulation based on energy balance, Poisson, and heat flow equations. In addition, a simple thermal circuit is proposed to model temperature variation in the SOI silicon film. The temperature distribution in the silicon film obtained from the proposed thermal-circuit model is in good agreement with the rigorous device simulation. In high driving current situations, temperature variation in the SOI silicon film is considerably large. Such a model will provide more useful information than the existing constant-temperature model.

Inclusion of Chemical-Mechanical Polishing Variation in Statistical Static Timing Analysis

Technology trends show the importance of modeling process variation in static timing analysis. With the advent of statistical static timing analysis (SSTA), multiple independent sources of variation can be modeled. This paper proposes a methodology for modeling metal interconnect process variation in SSTA. The developed methodology is applied in this study to investigate metal variation in SSTA resulting from chemical-mechanical polishing (CMP). Using our statistical methodology, we show that CMP variation has a smaller impact on chip performance as compared to other factors impacting metal process variation.