1,172 research outputs found

    Binning for IC Quality: Experimental Studies on the SEMATECH Data

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    The earlier smaller bipolar study did not provide a high enough bin 0 population to directly observe test escapes and thereby estimate defect levels for the best bin. Results presented here indicate that the best bin can be reasonably expected to show a 2 - 5 factor improvement in defect levels over the average for the lot for moderate to high yields (the overall yield for these experiments was approximately 65%). The experiments also confirm the dependence of the best bin quality on test transparency. The defect level improvement is poorer for the case Of IDDQ escapes where the tests applied had a much higher escape rate. Overall experimental results are consistent with analytical projections for typical values of the clustering parameter in [9]. The final version of this paper will include extensive analysis to validate the analytical models based on this data

    AI/ML Algorithms and Applications in VLSI Design and Technology

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    An evident challenge ahead for the integrated circuit (IC) industry in the nanometer regime is the investigation and development of methods that can reduce the design complexity ensuing from growing process variations and curtail the turnaround time of chip manufacturing. Conventional methodologies employed for such tasks are largely manual; thus, time-consuming and resource-intensive. In contrast, the unique learning strategies of artificial intelligence (AI) provide numerous exciting automated approaches for handling complex and data-intensive tasks in very-large-scale integration (VLSI) design and testing. Employing AI and machine learning (ML) algorithms in VLSI design and manufacturing reduces the time and effort for understanding and processing the data within and across different abstraction levels via automated learning algorithms. It, in turn, improves the IC yield and reduces the manufacturing turnaround time. This paper thoroughly reviews the AI/ML automated approaches introduced in the past towards VLSI design and manufacturing. Moreover, we discuss the scope of AI/ML applications in the future at various abstraction levels to revolutionize the field of VLSI design, aiming for high-speed, highly intelligent, and efficient implementations

    Yield modeling for deep sub-micron IC design

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    0.18?m high performance CMOS process optimization

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    Complementary metal oxide semiconductor (CMOS) is the most widely used discrete structure in the semiconductor sector. Low static power consumption, full-rail high/low voltage transfer characteristics as well as its ease of scaling creates the perfect combination for the high performance integrated circuits (IC). Today’s challenging semiconductor industry profile brings the deadlines earlier than expected as a result of the shorter time-to- market plans as well as limited lifetime on sophisticated ICs. Process optimization for manufacturability is one of the most challenging issues in the semiconductor industry since the adoption of the sub-micron CMOS technology. Process technologies often times gets released with- in tight project schedules without jeopardizing the quality and customer’s trust. Manufacturing facilities often times institute very strict process controls in order to achieve the quality and the high yields. At the same time they take the financial burden of throwing away the nonconforming material which does not meet the ir specifications. Improving the device performance becomes the responsibility of the Integration/Device engineering through a series of process characterization studies. This paper outlines the various 0.18 μm. CMOS technology issues such as threshold voltage and saturation current control, and proposes methods to optimize the process through a series of characterization studies. 6-Sigma-DMAIC process was explored in order to achieve the desired goal. Techniques described in this thesis could be used in any manufacturing or development environment
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