scholarly journals An Evolving Partial Consensus Fuzzy Collaborative Forecasting Approach

Mathematics ◽  
2020 ◽  
Vol 8 (4) ◽  
pp. 554 ◽  
Author(s):  
Tin-Chih Toly Chen ◽  
Yu-Cheng Wang ◽  
Chin-Hau Huang
Keyword(s):  
Random Access ◽  
Real Data ◽  
Product Yield ◽  
Percentage Error ◽  
Access Memory ◽  
Forecasting Accuracy ◽  
Meaningful Information ◽  
Forecasting Methods ◽  
Forecasting Performance ◽  
Partial Consensus

Current fuzzy collaborative forecasting methods have rarely considered how to determine the appropriate number of experts to optimize forecasting performance. Therefore, this study proposes an evolving partial-consensus fuzzy collaborative forecasting approach to address this issue. In the proposed approach, experts apply various fuzzy forecasting methods to forecast the same target, and the partial consensus fuzzy intersection operator, rather than the prevalent fuzzy intersection operator, is applied to aggregate the fuzzy forecasts by experts. Meaningful information can be determined by observing partial consensus fuzzy intersection changes as the number of experts varies, including the appropriate number of experts. We applied the evolving partial-consensus fuzzy collaborative forecasting approach to forecasting dynamic random access memory product yield with real data. The proposed approach forecasting performance surpassed current fuzzy collaborative forecasting that considered overall consensus, and it increased forecasting accuracy 13% in terms of mean absolute percentage error.

scholarly journals Modeling an Uncertain Productivity Learning Process Using an Interval Fuzzy Methodology

Mathematics ◽  
2020 ◽  
Vol 8 (6) ◽  
pp. 998 ◽  
Author(s):  
Min-Chi Chiu ◽  
Tin-Chih Toly Chen ◽  
Keng-Wei Hsu
Keyword(s):  
Learning Process ◽  
Random Access ◽  
Weighted Average ◽  
Absolute Error ◽  
Percentage Error ◽  
Lower And Upper Bounds ◽  
Access Memory ◽  
Major Drawback ◽  
Forecasting Accuracy ◽  
Ordered Weighted Average

Existing methods for forecasting the productivity of a factory are subject to a major drawback—the lower and upper bounds of productivity are usually determined by a few extreme cases, which unacceptably widens the productivity range. To address this drawback, an interval fuzzy number (IFN)-based mixed binary quadratic programming (MBQP)–ordered weighted average (OWA) approach is proposed in this study for modeling an uncertain productivity learning process. In the proposed methodology, the productivity range is divided into the inner and outer sections, which correspond to the lower and upper membership functions of an IFN-based fuzzy productivity forecast, respectively. In this manner, all actual values are included in the outer section, whereas most of the values are included within the inner section to fulfill different managerial purposes. According to the percentages of outlier cases, a suitable forecasting strategy can be selected. To derive the values of parameters in the IFN-based fuzzy productivity learning model, an MBQP model is proposed and optimized. Subsequently, according to the selected forecasting strategy, the OWA method is applied to defuzzify a fuzzy productivity forecast. The proposed methodology has been applied to the real case of a dynamic random access memory factory to evaluate its effectiveness. The experimental results indicate that the proposed methodology was superior to several existing methods, especially in terms of mean absolute error, mean absolute percentage error, and root mean square error in evaluating the forecasting accuracy. The forecasting precision achieved using the proposed methodology was also satisfactory.

Decomposition Methods for Tourism Demand Forecasting: A Comparative Study

2021 ◽  
pp. 004728752110361
Author(s):  
Chengyuan Zhang ◽  
Mingchen Li ◽  
Shaolong Sun ◽  
Ling Tang ◽  
Shouyang Wang
Keyword(s):  
Decomposition Method ◽  
Demand Forecasting ◽  
Decomposition Methods ◽  
Tourism Demand ◽  
Forecasting Accuracy ◽  
Forecasting Methods ◽  
Mode Decomposition ◽  
Forecasting Performance ◽  
Intrinsic Complexity ◽  
Comprehensive Comparison

Decomposition methods are extensively used for processing the complex patterns of tourism demand data. Given tourism demand data’s intrinsic complexity, it is critical to theoretically understand how different decomposition methods provide solutions. However, a comprehensive comparison of decomposition methods in tourism demand forecasting is still lacking. Hence, this study systematically investigates the forecasting performance of decomposition methods in tourism demand. Nine popular decomposition methods and six forecasting methods are employed, and their forecasting performance is compared. With Hong Kong visitor arrivals from eight major sources as a sample, three main conclusions are obtained from empirical results. First, all the decomposition methods generally outperform benchmark at all horizons, in both the level and directional forecasting. Second, decomposition methods can be divided into four categories based on forecasting accuracy. Finally, variational mode decomposition method is consistently superior to other eight decomposition methods and can provide the best forecasts in all cases.

Forecasting hotel demand uncertainty using time series Bayesian VAR models

2018 ◽  
Vol 25 (5) ◽  
pp. 734-756 ◽  
Author(s):  
Apostolos Ampountolas
Keyword(s):  
Demand Uncertainty ◽  
Error Rates ◽  
Percentage Error ◽  
Revenue Maximization ◽  
Var Models ◽  
Forecasting Accuracy ◽  
Bayesian Var ◽  
Vector Autoregressive ◽  
Forecasting Performance ◽  
The Mean

Demand uncertainty is a fundamental characteristic of the hospitality industry. Hotel room inventory is fixed, and devising an accurate daily demand measurement is a key operational challenge. In practice, it is difficult to predict the industry stability and capture demand uncertainty, so the industry relies on demand estimates. This process of estimation affects revenue maximization, as it is sensitive to incremental costs. In this article, we implemented vector autoregressive (VAR) models and compared them to the Bayesian VAR to examine the accuracy of predicting demand. We evaluated the results using a new measure of forecasting accuracy, the mean arctangent absolute percentage error (MAAPE). The results generated from the forecasts confirm the significant improvement in forecasting performance that can be obtained using the Bayesian model. It is noteworthy that the VAR performs the best for the lower horizons. The results also suggest that MAAPE outperforms other existing accuracy measures, in terms of error rates.

scholarly journals Trap-Related Reliability Problems of Dielectrics in Memory Cells

Electronics ◽  
2021 ◽  
Vol 10 (11) ◽  
pp. 1287
Author(s):  
Hiroshi Watanabe ◽  
Hsin-Jyun Lin
Keyword(s):  
Magnetic Properties ◽  
Random Access ◽  
Random Access Memory ◽  
Product Yield ◽  
Basic Mechanism ◽  
Static Random Access Memory ◽  
Electronic Charge ◽  
Memory Cells ◽  
Nand Flash ◽  
Access Memory

A basic mechanism for storing data in memory cells is to record changes in electronic charges, material phases, resistivities, magnetic properties, and so forth. The change in electronic charge has been widely used in the majority of mass-produced memories, such as dynamic random-access memory (DRAM), static random-access memory (SRAM), NOR Flash, and NAND Flash. Other emerging memories have collected widespread attention for acquiring extra advantages which cannot be achieved using the change in electronic charge. Many years of studies have told us that reliability problems are critically important in the development of both conventional and emerging memories, in order to improve the product yield. However, the topics related to these problems are too wide to cover in these limited pages. In this review chapter, we address several interesting examples of trap-related problems in dielectrics for use in various memory cells. For engineering purposes, it is very important to grasp the relation of the achieved physical intuitions and electronic characteristics of dielectrics.

Analysis of FSRAM Single Bit Failures Due to Unique Dislocations

1998 ◽  
Author(s):  
Phil Schani ◽  
S. Subramanian ◽  
Vince Soorholtz ◽  
Pat Liston ◽  
Jamey Moss ◽  
...  
Keyword(s):  
Electron Microscopy ◽  
Random Access ◽  
Random Access Memory ◽  
Static Random Access Memory ◽  
Temperature Sensitive ◽  
Wafer Level ◽  
Top Down ◽  
Access Memory ◽  
Transmission Electron ◽  
Contact Processing

Abstract Temperature sensitive single bit failures at wafer level testing on 0.4µm Fast Static Random Access Memory (FSRAM) devices are analyzed. Top down deprocessing and planar Transmission Electron Microscopy (TEM) analyses show a unique dislocation in the substrate to be the cause of these failures. The dislocation always occurs at the exact same location within the bitcell layout with respect to the single bit failing data state. The dislocation is believed to be associated with buried contact processing used in this type of bitcell layout.

Deprocessing Methodologies for Detection of IBC and Cell-to-Cell Shorts in Submicron DRAM

2012 ◽  
Author(s):  
Ramachandra Chitakudige ◽  
Sarat Kumar Dash ◽  
A.M. Khan
Keyword(s):  
High Selectivity ◽  
Electron Cyclotron Resonance ◽  
Random Access ◽  
Random Access Memory ◽  
Access Memory ◽  
Plasma System ◽  
Poly Silicon ◽  
Hazardous Gases ◽  
Wet Etch ◽  
Resonance Plasma

Abstract Detection of both Insufficient Buried Contact (IBC) and cell-to-cell short defects is quite a challenging task for failure analysis in submicron Dynamic Random Access Memory (DRAM) devices. A combination of a well-controlled wet etch and high selectivity poly silicon etch is a key requirement in the deprocessing of DRAM for detection of these types of failures. High selectivity poly silicon etch methods have been reported using complicated system such as ECR (Electron Cyclotron Resonance) Plasma system. The fact that these systems use hazardous gases like Cl2, HBr, and SF6 motivates the search for safer alternative deprocessing chemistries. The present work describes high selectivity poly silicon etch using simple Reactive Ion Etch (RIE) plasma system using less hazardous gases such as CF4, O2 etc. A combination of controlled wet etch and high selectivity poly silicon etch have been used to detect both IBC and cell-to-cell shorts in submicron DRAMs.

Challenges and Benefits of Product-Like SRAM in Technology Development

2011 ◽  
Author(s):  
Felix Beaudoin ◽  
Stephen Lucarini ◽  
Fred Towler ◽  
Stephen Wu ◽  
Zhigang Song ◽  
...  
Keyword(s):  
Technology Development ◽  
Random Access ◽  
Random Access Memory ◽  
Static Random Access Memory ◽  
Development Phase ◽  
High Complexity ◽  
Access Memory ◽  
Integration Schemes ◽  
Front End

Abstract For SRAMs with high logic complexity, hard defects, design debug, and soft defects have to be tackled all at once early on in the technology development while innovative integration schemes in front-end of the line are being validated. This paper presents a case study of a high-complexity static random access memory (SRAM) used during a 32nm technology development phase. The case study addresses several novel and unrelated fail mechanisms on a product-like SRAM. Corrective actions were put in place for several process levels in the back-end of the line, the middle of the line, and the front-end of the line. These process changes were successfully verified by demonstrating a significant reduction of the Vmax and Vmin nest array block fallout, thus allowing the broader development team to continue improving random defectivity.

Effect of Conductive Filament Temperature on ZrO2 based Resistive Random Access Memory Devices

2020 ◽  
Vol 12 (2) ◽  
pp. 02008-1-02008-4
Author(s):  
Pramod J. Patil ◽  
◽  
Namita A. Ahir ◽  
Suhas Yadav ◽  
Chetan C. Revadekar ◽  
...  
Keyword(s):  
Random Access ◽  
Random Access Memory ◽  
Resistive Random Access Memory ◽  
Memory Devices ◽  
Access Memory ◽  
Conductive Filament ◽  
Filament Temperature

scholarly journals Short-term load forecasting using Theta method

2019 ◽  
Vol 84 ◽  
pp. 01004 ◽  
Author(s):  
Grzegorz Dudek
Keyword(s):  
Empirical Studies ◽  
Load Forecasting ◽  
Seasonal Cycles ◽  
Short Term ◽  
Forecasting Accuracy ◽  
Forecasting Methods ◽  
Short Term Load Forecasting ◽  
Data Definition ◽  
System Data ◽  
Theta Method

The Theta method attracted the attention of researchers and practitioners in recent years due to its simplicity and superior forecasting accuracy. Its performance has been confirmed by many empirical studies as well as forecasting competitions. In this article the Theta method is tested in short-term load forecasting problem. The load time series expressing multiple seasonal cycles is decomposed in different ways to simplify the forecasting problem. Four variants of input data definition are considered. The standard Theta method is uses as well as the dynamic optimised Theta model proposed recently. The performances of the Theta models are demonstrated through an empirical application using real power system data and compared with other popular forecasting methods.

Sign in / Sign up

Export Citation Format

Share Document