Automated Linescan Analysis for CMP Modeling

Many of the process steps used in semiconductor chip manufacturing require planar (smooth) surfaces on the wafer to ensure correct pattern printing and generation of multilevel interconnections in the chips during manufacturing. Chemical-mechanical polishing/planarization (CMP) is the primary process used to achieve these surface planarity requirements. Modeling of CMP processes allows users to detect and fix large surface planarity variations (hotspots) in the layout prior to manufacturing. Fixing hotspots before tape-out may significantly reduce turnaround time and the cost of manufacturing. Creating an accurate CMP model that takes into account complicated chemical and mechanical polishing mechanisms is challenging. Measured data analysis and extraction of erosion and dishing data from profile linescans from test chips are important steps in CMP model building. Measured linescans are often tilted and noisy, which makes the extraction of erosion and dishing data more difficult. The development and implementation of algorithms used to perform automated linescan analysis may significantly reduce CMP model building time and improve the accuracy of the models. In this work, an automated linescan analysis (ALSA) tool is presented that performs automated linescan delineation, test pattern separation, and automatic extraction of erosion and dishing values from linescan data.

A Study on the Pattern Effects of Chemical Mechanical Planarization with CNN-Based Models

Chemical mechanical polishing (CMP) has become one of the most important process stages in the fabrication of advanced integrated circuits (IC). The CMP pattern effect strongly influences the planarization of the chip surface morphology after CMP, degrading the performance and the yield of the circuits. In this paper, we introduce a method to predict the post-CMP surface morphology with a convolutional neural network (CNN)-based CMP model. Then, CNN-based, density step height (DSH)-based, and common neural-network-based CMP models are built to compare the accuracy of the predictions. The test chips are designed and taped out and the predictions of the three models are compared with experimental results measured by an atomic force profiler (AFP) and scanning electron microscope (SEM). The results show that CNN-based CMP models have better accuracy by taking advantage of the CNN networks to extract features from images instead of the traditional equivalent pattern parameters. The effective planarization length (EPL) is introduced and defined to make better predictions with real-time CMP models and in dummy filling tasks. Experiments are designed to show a method to solve the EPL.

A STRATEGIC PLAN FOR DEVELOPING THE BANDA ARCHIPELAGO AS AN ECOTOURISM AND ENVIRONMENTAL CONSERVATION AREAS

Banda Islands is located in the eastern part of Indonesia. These islands are rich in coral reefs and fish. This archipelago has been designated by the Indonesian Government as a tourist area. However, the Banda Islands are facing several economic, social and environmental problems. This study aims to provide policy input to local governments in the form of a strategic plan to develop the Banda Islands as ecotourism and environmental conservation. The method used is “Ecotourism Opportunity Spectrum” (ECOS) and the “Conservation Measures Partnership" (CMP) model. The results of this study produce five strategic plans: a) The first priority is to create a working forum; b) the second is the integration of tasks between the parties involved; c) the third priority is the regulation for changes in coastal land; d) fourth priority is mapping of fishing and; e) the fifth priority is alternative livelihoods.

Ownership feature and firm performance via corporate innovation performance

Purpose The purpose of this paper is to investigate the simultaneous relationship among ownership concentration, innovation and firm performance of the small- and medium-sized enterprises (SMEs) in Vietnam during the 2011–2015. By employing a Conditional Mixed Process (CMP) model, the findings show that: there is no impact of ownership concentration on innovation, but it has a positive impact on sales growth; innovation positively affects firm performance; and there exists a positively reverse causality from sales growth to innovation. Design/methodology/approach In this study, the authors propose the adaption of CMP model (Roodman, 2011). The nature of the first stage dependent variable – Innovation – is a binary one while the dependent variable Performance is continuous. Therefore, a model that can adapt the binary nature of the dependent variable and perform the estimation of a system of equations such as CMP model is preferred. The CMP framework is substantially that of seemingly unrelated regression, but with application in a larger scope. This approach is based on a “simulated maximum likelihood method” suggested by Geweke–Hajivassiliou–Keane algorithm. Findings By applying CMP method, this study examines the simultaneous relationship among ownership concentration, innovation and firm performance of the SMEs in Vietnam from 2011 to 2015. The findings indicate that: there is no impact of ownership concentration on innovation, but it has a positive impact on sales growth; innovation positively affects firm performance; and there exists a positively reverse causality from sales growth to innovation. Research limitations/implications In spite of the efforts to explore the simultaneous relationship among ownership concentration, innovation and firm performance of the SMEs in Vietnam, the study still has some limitations which are promising further research directions. First, the SME surveys by Central Institute for Economic Management do not have much information about other types of ownership including state-owned and foreign ownership. Therefore, possible further studies with richer data sets may explore the impacts of different types of ownership on firm innovation and performance. Second, other types of innovation such as organizational innovation, marketing innovation can also be investigated in further studies in a richer data set for the case of Vietnam SMEs. Originality/value The findings show that: there is no impact of ownership concentration on innovation, but it has a positive impact on sales growth; innovation positively affects firm performance; and there exists a positively reverse causality from sales growth to innovation. The policy implications insist on facilitating SMEs with easier access to capital via loans with preferred interest or trust loans without collateral, training programs for the labor force and SME leaders, and reduction of unnecessary administrative procedure.

Modeling stimulus-dependent variability improves decoding of population neural responses

Neural responses to repeated presentations of an identical stimulus often show substantial trial-to-trial variability. How the mean firing rate varies in response to different stimuli or during different movements (tuning curves) has been extensively modeled in a wide variety of neural systems. However, the variability of neural responses can also have clear tuning independent of the tuning in the mean firing rate. This suggests that the variability could contain information regarding the stimulus/movement beyond what is encoded in the mean firing rate. Here we demonstrate how taking variability into account can improve neural decoding. In a typical neural coding model spike counts are assumed to be Poisson with the mean response depending on an external variable, such as a stimulus or movement. Bayesian decoding methods then use the probabilities under these Poisson tuning models (the likelihood) to estimate the probability of each stimulus given the spikes on a given trial (the posterior). However, under the Poisson model, spike count variability is always exactly equal to the mean (Fano factor = 1). Here we use two alternative models - the Conway-Maxwell-Poisson (CMP) model and Negative Binomial (NB) model - to more flexibly characterize how neural variability depends on external stimuli. These models both contain the Poisson distribution as a special case but have an additional parameter that allows the variance to be greater than the mean (Fano factor >1) or, for the CMP model, less than the mean (Fano factor <1). We find that neural responses in primary motor (M1), visual (V1), and auditory (A1) cortices have diverse tuning in both their mean firing rates and response variability. Across cortical areas, we find that Bayesian decoders using the CMP or NB models improve stimulus/movement estimation accuracy by 4-12% compared to the Poisson model. Moreover, the uncertainty of the non-Poisson decoders more accurately reflects the magnitude of estimation errors. In addition to tuning curves that reflect average neural responses, stimulus-dependent response variability may be an important aspect of the neural code. Modeling this structure could, potentially, lead to improvements in brain machine interfaces.