Interconnect yield analysis and fault tolerance for field programmable gate arrays

Imperial Users onl

Choose-Your-Own Adventure: A Lightweight, High-Performance Approach To Defect And Variation Mitigation In Reconfigurable Logic

For field-programmable gate arrays (FPGAs), fine-grained pre-computed alternative configurations, combined with simple test-based selection, produce limited per-chip specialization to counter yield loss, increased delay, and increased energy costs that come from fabrication defects and variation. This lightweight approach achieves much of the benefit of knowledge-based full specialization while reducing to practical, palatable levels the computational, testing, and load-time costs that obstruct the application of the knowledge-based approach. In practice this may more than double the power-limited computational capabilities of dies fabricated with 22nm technologies. Contributions of this work: • Choose-Your-own-Adventure (CYA), a novel, lightweight, scalable methodology to achieve defect and variation mitigation • Implementation of CYA, including preparatory components (generation of diverse alternative paths) and FPGA load-time components • Detailed performance characterization of CYA – Comparison to conventional loading and dynamic frequency and voltage scaling (DFVS) – Limit studies to characterize the quality of the CYA implementation and identify potential areas for further optimizatio

Design and application of reconfigurable circuits and systems

Open Acces

GROK-FPGA: Generating Real on-Chip Knowledge for FPGA Fine-Grain Delays Using Timing Extraction

Circuit variation is one of the biggest problems to overcome if Moore\u27s Law is to continue. It is no longer possible to maintain an abstraction of identical devices without huge yield losses, performance penalties, and energy costs. Current techniques such as margining and grade binning are used to deal with this problem. However, they tend to be conservative, offering limited solutions that will not scale as variation increases. Conventional circuits use limited tests and statistical models to determine the margining and binning required to counteract variation. If the limited tests fail, the whole chip is discarded. On the other hand, reconfigurable circuits, such as FPGAs, can use more fine-grained, aggressive techniques that carefully choose which resources to use in order to mitigate variation. Knowing which resources to use and avoid, however, requires measurement of underlying variation. We present Timing Extraction, a methodology that allows measurement of process variation without expensive testers nor highly invasive techniques, rather, relying only on resources already available on conventional FPGAs. It takes advantage of the fact that we can measure the delay of logic paths between any two registers. Measuring enough paths, provides the information necessary to decompose the delay of each path into individual components-essentially, forming a system of linear equations. Determining which paths to measure requires simple graph transformation algorithms applied to a representation of the FPGA circuit. Ultimately, this process decomposes the FPGA into individual components and identifies which paths to measure for computing the delay of individual components. We apply Timing Extraction to 18 commercially available Altera Cyclone III (65 nm) FPGAs. We measure 22×28 logic clusters and the interconnect within and between cluster. Timing Extraction decomposes this region into 1,356,182 components, classified into 10 categories, requiring 2,736,556 path measurements. With an accuracy of ±3.2 ps, our measurements reveal regional variation on the order of 50 ps, systematic variation from 30 ps to 70 ps, and random variation in the clusters with σ=15 ps and in the interconnect with σ=62 ps

REDUCING POWER DURING MANUFACTURING TEST USING DIFFERENT ARCHITECTURES

Power during manufacturing test can be several times higher than power consumption in functional mode. Excessive power during test can cause IR drop, over-heating, and early aging of the chips. In this dissertation, three different architectures have been introduced to reduce test power in general cases as well as in certain scenarios, including field test. In the first architecture, scan chains are divided into several segments. Every segment needs a control bit to enable capture in a segment when new faults are detectable on that segment for that pattern. Otherwise, the segment should be disabled to reduce capture power. We group the control bits together into one or more control chains. To address the extra pin(s) required to shift data into the control chain(s) and significant post processing in the first architecture, we explored a second architecture. The second architecture stitches the control bits into the chains they control as EECBs (embedded enable capture bits) in between the segments. This allows an ATPG software tool to automatically generate the appropriate EECB values for each pattern to maintain the fault coverage. This also works in the presence of an on-chip decompressor. The last architecture focuses primarily on the self-test of a device in a 3D stacked IC when an existing FPGA in the stack can be programmed as a tester. We show that the energy expended during test is significantly less than would be required using low power patterns fed by an on-chip decompressor for the same very short scan chains

MICROELECTRONICS PACKAGING TECHNOLOGY ROADMAPS, ASSEMBLY RELIABILITY, AND PROGNOSTICS

This paper reviews the industry roadmaps on commercial-off-the shelf (COTS) microelectronics packaging technologies covering the current trends toward further reducing size and increasing functionality. Due tothe breadth of work being performed in this field, this paper presents only a number of key packaging technologies. The topics for each category were down-selected by reviewing reports of industry roadmaps including the International Technology Roadmap for Semiconductor (ITRS) and by surveying publications of the International Electronics Manufacturing Initiative (iNEMI) and the roadmap of association connecting electronics industry (IPC). The paper also summarizes the findings of numerous articles and websites that allotted to the emerging and trends in microelectronics packaging technologies. A brief discussion was presented on packaging hierarchy from die to package and to system levels. Key elements of reliability for packaging assemblies were presented followed by reliabilty definition from a probablistic failure perspective. An example was present for showing conventional reliability approach using Monte Carlo simulation results for a number of plastic ball grid array (PBGA). The simulation results were compared to experimental thermal cycle test data. Prognostic health monitoring (PHM) methods, a growing field for microelectronics packaging technologies, were briefly discussed. The artificial neural network (ANN), a data-driven PHM, was discussed in details. Finally, it presented inter- and extra-polations using ANN simulation for thermal cycle test data of PBGA and ceramic BGA (CBGA) assemblies

AI/ML Algorithms and Applications in VLSI Design and Technology

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

A complete design path for the layout of flexible macros

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