With the continued down-scaling of IC technology and increase in manufacturing process variations, it is becoming ever more difficult to accurately estimate circuit performance of manufactured devices. This poses significant challenges on the effective application of adaptive voltage scaling (AVS) which is widely used as the most important power optimization method in modern devices. Process variations specifically limit the capabilities of Process Monitoring Boxes (PMBs), which represent the current industrial state-of-the-art AVS approach. To overcome this limitation, in this paper we propose an alternative solution using delay testing, which is able to eliminate the need for PMBs, while improving the accuracy of voltage estimation. The paper shows, using simulation of ISCAS’99 benchmarks with 28nm FD-SOI library, that using delay test patterns result in an error of 5.33% for transition fault testing (TF), error of 3.96% for small delay defect testing (SDD), and an error as low as 1.85% using path delay testing (PDLY). In addition, the paper also shows the impact of technology scaling on the accuracy of delay testing for performance estimation during production. The results show that the 65nm technology node exhibits the same trends identified for the 28nm technology node, namely that PDLY is the most accurate, while, TF is the least accurate performance estimator.

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In deep sub-micron technologies, the increasing effect of process and environmental variations has lead chip manufacturers to use adaptive voltage scaling techniques in order to adapt operation parameters exclusively to each chip. The increasing effect of process variation is limiting the effectiveness of current chip monitoring approaches, such as on-chip performance monitor boxes (PMBs), which results in yield loss and high design margins, thus high power consumption. This paper proposes an alternative solution for adaptive voltage scaling using delay test patterns, which is able to eliminate the need for PMBs, and thus the long expensive characterization phase of tuning PMBs to each design, while improving the yield as well as power optimization. Results show, using an industrial grade 28nm FD-SOI library developed for low power devices, that delay testing for performance prediction reduces the inaccuracy down to 1.85%@en

Application of manufacturing testing during the production process of integrated circuits is considered essential to ensure the quality of the devices used in the field. However, it is desirable to use the information gathered during the test process to add value to other aspects of the manufacturing process. This paper proposes a method to use path delay (PDLY) test patterns, not only to validate the functionality of the devices, but also as an alternative solution for performance estimation, that can be used for offline adaptive voltage scaling. This approach has many advantages over the currently used industrial performance estimation methods, so-called performance monitoring boxes (PMBs). Using simulation of ISCAS'99 benchmarks with 28nm FD-SOI libraries, the paper shows that the PDLY based approach reduces the inaccuracy of performance prediction from 2.32% (achieved by the classic PMB approach) to 1.85%, without the need for any on-chip monitors.

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To overcome the increasing sensitivity to variability in nanoscale integrated circuits, operation parameters (e.g., supply voltage) are adapted in a customized way exclusively to each chip. AVS is a standard industrial technique which has been adopted widely to compensate for process, voltage, and temperature variations as well as power optimization of integrated circuits. For cost and complexity reasons, AVS techniques are usually implemented by means of on-chip performance monitors (so-called PMBs) allowing fast performance evaluation during production or run time. Such on-chip monitoring approaches estimate operation parameters either based on responses from performance monitors with no interaction with the circuit or by monitoring the actual critical paths of the circuit. In this thesis, we focus on AVS techniques, which estimate operation parameters using responses from on-chip performance monitors with no interaction with the circuit during production. We discuss the challenges that these monitoring methodologies face with decreasing node sizes, in terms of accuracy and effectiveness. We show that the accuracy of these approaches is design dependent, and requires up to 15% added design margin. In addition, we show using silicon measurements of a nanometric FD-SOI device that the required design margin is above 10% of the clock cycle, which leads to significant waste of power. In this thesis, we introduce the new method of using delay test patterns including TF, SDD, and PDLY test patterns for application of AVS during IC production. The proposed method is able to eliminate the need for PMBs, while improving the accuracy of performance estimation. The basic requirement of using delay-based AVS is that there should be a reasonable correlation between the frequency the chip can attain while passing all delay test patterns and the actual frequency of the chip. Based on simulation results of ISCAS’99 benchmarks with a 28 nm FD-SOI library, using delay test patterns result in an error of 5.33% for TF testing, an error of 3.96% for SDD testing, and an error as low as 1.85% using PDLY testing. Accordingly, PDLY patterns have the capacity to achieve the lowest error in performance estimation, followed by SDD patterns and finally TF patterns. We performed the same analysis using a 65 nm technology node, which showed the same results. We also did two different silicon measurements on a 28 nm FD-SOI CPU to investigate the effectiveness of the TF-based approach. The results of the first case study on real silicon comparing the performance estimation using functional test patterns and the TF-based approach show a very close correlation between the two, which proves the effectiveness of the TF approach. The second case study compares the accuracy of voltage estimation using PMBs and the TF-based approach. The results show that the PMB approach can only account for 85% of the uncertainty in voltage measurements, which results in considerable power waste. In comparison, the TF-based approach can account for 99% of that uncertainty, thereby providing the ability to reducing that wasted power.@en

Adaptive voltage scaling (AVS) has been used widely to compensate for process, voltage, and temperature variations as well as power optimization of integrated circuits. The
current industrial state-of-the-art AVS approaches using Process Monitoring Boxes (PMBs) have shown several limitations such as huge characterization effort, which makes these approaches very expensive, and a low accuracy that results in extra margins, which consequently lead to yield loss and performance limitations.
To overcome those limitations, in this paper we propose an alternative solution using transition fault test patterns, which is able to eliminate the need for PMBs, while improving the accuracy of voltage estimation. The paper shows, using simulation of ISCAS’99 benchmarks with 28nm FD-SOI library, that AVS using transition fault testing (TF-based AVS) results in an error as low as 5.33%. The paper also shows that the PMB approach can only account for 85% of the uncertainty in voltage measurements, which results in power waste, while the TF-based approach can account for 99% of that uncertainty.@en

Process variation occurring during fabrication of complex VLSI devices induce uncertainties in operation parameters (e.g., supply voltage) to be applied to each device in order for it to fit within the allowed power budget and get the optimum
power efficiency. Therefore, an efficient post manufacturing performance estimation mechanism is needed in order to tune operation parameters for each device during production. The current state-of-the-art approach of using Process Monitoring Boxes (PMBs) have shown some limitations in terms of cost and accuracy that limit their benefit. Simulation results on ISCAS'99 benchmarks using 28nm FD-SOI library show that the accuracy of PMB approaches is design dependent, and requires up to 8.20 % added design margin. To overcome those limitations, in this paper we propose an alternative solution using transition
fault (TF) test patterns, which is able to eliminate the need for PMBs, while improving the accuracy of performance estimation. The paper discusses a case study on real silicon comparing the performance estimation using functional test patterns and the TF based approach on a 28nm FD-SOI CPU. The results show a very close correlation between TF test patterns and functional patterns.@en

Circuit monitoring techniques have been adopted widely to compensate for process, voltage, and temperature variations as well as power optimization of integrated circuits. For cost and complexity reasons, these techniques are usually implemented by means of performance monitors allowing fast performance evaluation during production. In this paper, we demonstrate the limitations of performance monitoring methodologies in terms of accuracy and effectiveness. Silicon measurements of a nanometric FD-SOI device show that the required design margin is above 10% of the clock cycle, which leads to unacceptable waste of
power.@en