Continuous process-level and system-level innovation has driven Moore's Law scaling for the last fifty years, and will continue to do so in the next decades. In the last two decades, there has been an acceleration of new materials and devices into semiconductor manufacturing, such as low-k, strained Si, high-k, and FinFET, in order to continue process and cost scaling. At the same time, ever increasing component integration on SoCs has further driven cost scaling, allowing the current mobile era to take shape. In the next decade, the focus of SoC innovation will be on patterning and low-resistance materials on the process side, and multi-die package integration on the system side.

On-demand digital print service is a form of personalized manufacturing service. Key to the commercial print value-creation chain is the responsive and accurate knowledge discovery and decision making throughout the service engagement and fulfillment between the content suppliers and the print service providers (PSP). The business model of on-demand print imposes a great challenge to the PSP factory design, production planning and management due to its intrinsic, highly volatile demand stream. It pushes the PSP resource planning and production management from a form of “tribal art” towards data driven management science. Operations simulation and its practice is an integral component of the data-driven decision making process, and is gaining growing significance. In this paper, we describe operations simulation of an end-to-end digital print process; we treat the management of print manufacturing as a heterogeneous, concurrent, integrated system, accounting for the performance, efficiency, stability, and sustainability as organic system attributes. We exhibit the comparison between the simulation results and the factory internal audit information and show good agreement.

Except for linear devices like CRTs, color transformations from colorimetric specifications to device coordinates are mostly obtained by measuring a set of samples, inverting the table, and looking up values in the table (including interpolation), and mapping the gamut from input to output device.The accuracy of a transformation is determined by reproducing a second set of samples and measuring the reproduction errors. Accuracy as the average predicted perceptual error is then used as a metric for quality. Accuracy and precision are important metrics in commercial print because a print service provider can charge a higher price for more accurate color, or can widen his tolerances when customers prefer cheap prints.The disadvantage of determining tolerances through averaging perceptual errors is that the colors in the sample sets are independent and this is not necessarily a good correlate of print quality as determined through psychophysics studies. Indeed, images consist of color palettes and the main quality factor is not color fidelity but color integrity. For example, if the divergence of the field of error vectors is zero, color constancy is likely to take over and humans will perceive the color reproduction as being of good quality, even if the average error is relatively large. However, if the errors are small but in random directions, the perceived image quality is poor because the relation among colors is altered.We propose a standard practice to determine tolerance based on the Farnsworth-Munsell 100-hue test (FM-100) for the second set and to evaluate the color transpositions-a metric for color integrity-instead of the color differences. The quality metric is then the FM-100 score. There are industry standards for the tolerances of color judges, and the same tolerances and classification can be use for print workflows or its components (e. g., presses, proofers, displays). We generalize this practice to arbitrary perceptually uniform scales tailored to specific applications and present an implementation.In essence, we propose to extend the color discrimination test procedures used to evaluate human observers, to mechanical and electronic color reproduction devices.

Digital commercial print providers are increasingly seeing more and more low-value, very short-run orders as a result of personalization and customization of content. In addition, consolidation due to margin pressure and low turnaround times necessitates a quicker reaction to scenarios such as acquiring additional capacity in the peak season or off-loading capacity during the off-peak season. Traditional tools for negotiating and acquiring customer orders are increasingly becoming prohibitive in this environment due to their high costs and lack of ability to make rapid changes. Tools that use machine readable service level agreements (SLAs) promise easier management of customer orders, but are currently not widely used in manufacturing domains such as digital commercial printing. If SLAs are to appear in manufacturing and other related domains, they will need to deal with both SLA Monitoring and SLA Negotiations in the same framework. Programming languages have long used the concept of Types to guarantee behavior of programs. In this paper we show that there is a deep connection between Types and SLAs. The connection stems from the fact that both the Types as well as the SLAs are inherent guarantees about the run-time behavior. The mapping between Types and SLAs is shown by formulating problems in both the domains using notations which have similar semantics. In particular, we show: 1) SLA Monitoring has a parallel in Type Checking, 2) SLA Negotiation has a parallel in Type Inference, and 3) SLA Inhabitation has a parallel in Type Inhabitation. We also briefly mention how the rich meta-theorems about types such as preservation, progress and replacement theorems can be used to reason about SLAs, especially for services which deliver manufactured products.

In this paper, we investigate the suitability of the GPU for a parallel implementation of the pinwheel error diffusion. We demonstrate a high-performance GPU implementation by efficiently parallelizing and unrolling the image processing algorithm. Our GPU implementation achieves a 10 - 30x speedup over a two-threaded CPU error diffusion implementation with comparable image quality. We have conducted experiments to study the performance and quality tradeoffs for differences in image block sizes. We also present a performance analysis at assembly level to understand the performance bottlenecks.