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.

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 A Modified Lotka–Volterra Model for Diffusion and Substitution of Multigeneration DRAM Processing Technologies

2017 ◽  
Vol 2017 ◽  
pp. 1-12
Author(s):  
Hui-Chih Hung ◽  
Yu-Chih Chiu ◽  
Muh-Cherng Wu
Keyword(s):  
Market Share ◽  
New Technologies ◽  
Random Access ◽  
Absolute Error ◽  
Volterra Model ◽  
Access Memory ◽  
Processing Technologies ◽  
Volterra Models ◽  
Share Data ◽  
The Mean

We attempt to develop an effective forecasting model for the diffusion and substitution of multigeneration Dynamic Random Access Memory (DRAM) processing technologies. We consider market share data and propose a modified Lotka–Volterra model, in which an additional constraint on the summation of market share is introduced. The mean absolute error is used to measure the accuracy of our market share predictions. Market share data in DRAM industries from quarter one (Q1) of 2005 to 2013 Q4 is collected to validate the prediction accuracy. Our model significantly outperforms other benchmark forecasting models of both revenue and market share data, including the Bass and Lotka–Volterra models. Compared to prior studies on forecasting the diffusion and substitution of multigeneration technologies, our model has two new perspectives: (1) allowing undetermined number of multigeneration technologies and inconsecutive adoption of new technologies and (2) requiring less data for forecasting newborn technologies.

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 Improved Device Distribution in High-Performance SiNx Resistive Random Access Memory via Arsenic Ion Implantation

Nanomaterials ◽  
2021 ◽  
Vol 11 (6) ◽  
pp. 1401
Author(s):  
Te Jui Yen ◽  
Albert Chin ◽  
Vladimir Gritsenko
Keyword(s):  
High Performance ◽  
Conduction Mechanism ◽  
Random Access ◽  
Random Access Memory ◽  
Resistive Random Access Memory ◽  
Switching Behavior ◽  
Access Memory ◽  
Current Voltage ◽  
Current Voltage Characteristics ◽  
Limited Conduction

Large device variation is a fundamental challenge for resistive random access memory (RRAM) array circuit. Improved device-to-device distributions of set and reset voltages in a SiNx RRAM device is realized via arsenic ion (As+) implantation. Besides, the As+-implanted SiNx RRAM device exhibits much tighter cycle-to-cycle distribution than the nonimplanted device. The As+-implanted SiNx device further exhibits excellent performance, which shows high stability and a large 1.73 × 103 resistance window at 85 °C retention for 104 s, and a large 103 resistance window after 105 cycles of the pulsed endurance test. The current–voltage characteristics of high- and low-resistance states were both analyzed as space-charge-limited conduction mechanism. From the simulated defect distribution in the SiNx layer, a microscopic model was established, and the formation and rupture of defect-conductive paths were proposed for the resistance switching behavior. Therefore, the reason for such high device performance can be attributed to the sufficient defects created by As+ implantation that leads to low forming and operation power.

scholarly journals All-inorganic perovskite quantum dot light-emitting memories

2021 ◽  
Vol 12 (1) ◽  
Author(s):  
Meng-Cheng Yen ◽  
Chia-Jung Lee ◽  
Kang-Hsiang Liu ◽  
Yi Peng ◽  
Junfu Leng ◽  
...  
Keyword(s):  
Random Access ◽  
Optoelectronic Device ◽  
Resistive Random Access Memory ◽  
Access Memory ◽  
Light Emitting ◽  
Inorganic Perovskite ◽  
Emission Color ◽  
Perovskite Quantum Dots ◽  
In Series ◽  
First Time

AbstractField-induced ionic motions in all-inorganic CsPbBr3 perovskite quantum dots (QDs) strongly dictate not only their electro-optical characteristics but also the ultimate optoelectronic device performance. Here, we show that the functionality of a single Ag/CsPbBr3/ITO device can be actively switched on a sub-millisecond scale from a resistive random-access memory (RRAM) to a light-emitting electrochemical cell (LEC), or vice versa, by simply modulating its bias polarity. We then realize for the first time a fast, all-perovskite light-emitting memory (LEM) operating at 5 kHz by pairing such two identical devices in series, in which one functions as an RRAM to electrically read the encoded data while the other simultaneously as an LEC for a parallel, non-contact optical reading. We further show that the digital status of the LEM can be perceived in real time from its emission color. Our work opens up a completely new horizon for more advanced all-inorganic perovskite optoelectronic technologies.

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