3 research outputs found

    Voltage fluctuations in IC power supply distribution

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    The supply voltage decrease and power consumption increase of modern ICs made the requirements for low voltage fluctuation caused by packaging and on-chip parasitic impedances more difficult to achieve. Most of the research works on the area assume that all the nodes of the chip are fed at the same voltage, in such a way that the main cause of disturbance or fluctuation is the parasitic impedance of packaging. In the paper an approach to analyze the effect of high and fast current demands on the on-chip power supply network. First an approach to model the entire network by considering a homogeneous conductive foil is presented. The modification of the timing parameters of flipflops caused by spatial voltage drops through the IC surface are also investigated.Peer Reviewe

    Low Cost Power and Supply Noise Estimation and Control in Scan Testing of VLSI Circuits

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    Test power is an important issue in deep submicron semiconductor testing. Too much power supply noise and too much power dissipation can result in excessive temperature rise, both leading to overkill during delay test. Scan-based test has been widely adopted as one of the most commonly used VLSI testing method. The test power during scan testing comprises shift power and capture power. The power consumed in the shift cycle dominates the total power dissipation. It is crucial for IC manufacturing companies to achieve near constant power consumption for a given timing window in order to keep the chip under test (CUT) at a near constant temperature, to make it easy to characterize the circuit behavior and prevent delay test over kill. To achieve constant test power, first, we built a fast and accurate power model, which can estimate the shift power without logic simulation of the circuit. We also proposed an efficient and low power X-bit Filling process, which could potentially reduce both the shift power and capture power. Then, we introduced an efficient test pattern reordering algorithm, which achieves near constant power between groups of patterns. The number of patterns in a group is determined by the thermal constant of the chip. Experimental results show that our proposed power model has very good correlation. Our proposed X-Fill process achieved both minimum shift power and capture power. The algorithm supports multiple scan chains and can achieve constant power within different regions of the chip. The greedy test pattern reordering algorithm can reduce the power variation from 29-126 percent to 8-10 percent or even lower if we reduce the power variance threshold. Excessive noise can significantly affect the timing performance of Deep Sub-Micron (DSM) designs and cause non-trivial additional delay. In delay test generation, test compaction and test fill techniques can produce excessive power supply noise. This can result in delay test overkill. Prior approaches to power supply noise aware delay test compaction are too costly due to many logic simulations, and are limited to static compaction. We proposed a realistic low cost delay test compaction flow that guardbands the delay using a sequence of estimation metrics to keep the circuit under test supply noise more like functional mode. This flow has been implemented in both static compaction and dynamic compaction. We analyzed the relationship between delay and voltage drop, and the relationship between effective weighted switching activity (WSA) and voltage drop. Based on these correlations, we introduce the low cost delay test pattern compaction framework considering power supply noise. Experimental results on ISCAS89 circuits show that our low cost framework is up to ten times faster than the prior high cost framework. Simulation results also verify that the low cost model can correctly guardband every path‟s extra noise-induced delay. We discussed the rules to set different constraints in the levelized framework. The veto process used in the compaction can be also applied to other constraints, such as power and temperature

    Machine learning support for logic diagnosis

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