The Experts below are selected from a list of 292062 Experts worldwide ranked by ideXlab platform
A Asenov - One of the best experts on this subject based on the ideXlab platform.
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3D electro-thermal simulations of bulk FinFETs with statistical variations
2015 International Conference on Simulation of Semiconductor Processes and Devices (SISPAD), 2015Co-Authors: Liping Wang, Binjie Cheng, C Millar, Andrew R. Brown, Mihail Nedjalkov, C. Alexander, A AsenovAbstract:This paper investigates the impact of self-heating on the statistical variability of bulk FinFETs. 3D electro-thermal simulations have been performed using the GSS statistical-variability-aware device simulator GARAND, recently enhanced with a thermal simulation module. A bulk FinFET, designed to meet the specifications for the 14/16nm CMOS Technology Generation, is used as a test bed, taking into account the combined effects of gate edge roughness, fin edge roughness and metal gate granularity. The statistical distribution of key figures of merit, especially the on-current, under the influence of thermal effects, is analysed.
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FinFET Centric Variability-Aware Compact Model Extraction and Generation Technology Supporting DTCO
IEEE Transactions on Electron Devices, 2015Co-Authors: Xingsheng Wang, Binjie Cheng, C Millar, David Reid, Andrew Pender, Plamen Asenov, A AsenovAbstract:In this paper, we present a FinFET-focused variability-aware compact model (CM) extraction and Generation Technology supporting design-Technology co-optimization. The 14-nm CMOS Technology Generation silicon on insulator FinFETs are used as testbed transistors to illustrate our approach. The TCAD simulations include a long-range process-induced variability using a design of experiment approach and short-range purely statistical variability (mismatch). The CM extraction supports a hierarchical CM approach, including nominal CM extraction, response surface CM extraction, and statistical CM extraction. The accurate CM Generation Technology captures the often non-Gaussian distributions of the key transistor figures of merit and their correlations preserving also the correlations between process and statistical variability. The use of the hierarchical CM is illustrated in the simulation of FinFET-based SRAM cells and ring oscillators.
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Impact of NBTI/PBTI on SRAM Stability Degradation
IEEE Electron Device Letters, 2011Co-Authors: Binjie Cheng, Andrew R. Brown, A AsenovAbstract:We investigate the impact of negative-bias temperature instability (NBTI) on the degradation of the static noise margin (SNM) and write noise margin (WNM) of a SRAM cell. This is based on the quantitative simulation of the statistical impact of NBTI on p-MOSFETs corresponding to a 45-nm low-power Technology Generation. Due to the increasing importance of positive-bias temperature instability (PBTI) of n-MOSFETs with the introduction of high-/v/metal gate stacks, we also explore the additional impact of PBTI on statistical SNM and WNM degradation behavior. The results indicate that NBTI-only induced SNM and WNM degradations follow different evolutionary patterns compared to the impact of simultaneous NBTI and PBTI degradation, and high distribution moment information is required for the reconstruction of noise margin distributions.
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Evaluation of statistical variability in 32 and 22 nm Technology Generation LSTP MOSFETs
2009Co-Authors: Binjie Cheng, C Millar, Andrew R. Brown, Scott Roy, A AsenovAbstract:The quantitative evaluation of the impact of key sources of static and dynamic statistical variability (SV) are presented for LSTP nMOSFETs corresponding to 32 nm and 22 nm Technology Generation transistors with thin-body (TB) SOI and double gate (DG) architectures, respectively. The simulation results indicate that TB SOI and DG devices are not only more resistant to random dopant induced variability compared to their bulk counterparts, but are also more tolerant to line edge roughness induced variability. However, the improved static SV performance shifts the emphasis to dynamic SV introduced by trapped charge associated with aging processes.
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evaluation of intrinsic parameter fluctuations on 45 32 and 22nm Technology node lp n mosfets
European Solid-State Device Research Conference, 2008Co-Authors: Binjie Cheng, A R Brown, S. Roy, C Millar, A AsenovAbstract:The quantitative evaluation of the impact of key sources of statistical variability (SV) are presented for LP nMOSFETs corresponding to 45 nm, 32 nm and 22 nm Technology Generation transistors with bulk, thin body (TB) SOI and double gate (DG) device architectures respectively. The simulation results indicate that TBSOI and DG are not only resistant to random dopant induced variability, but also are more tolerant to line edge roughness induced variability. Even two Technology Generations ahead from their bulk counterparts, DG MOSFETs will still have 4 times less variability than bulk devices.
Binjie Cheng - One of the best experts on this subject based on the ideXlab platform.
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3D electro-thermal simulations of bulk FinFETs with statistical variations
2015 International Conference on Simulation of Semiconductor Processes and Devices (SISPAD), 2015Co-Authors: Liping Wang, Binjie Cheng, C Millar, Andrew R. Brown, Mihail Nedjalkov, C. Alexander, A AsenovAbstract:This paper investigates the impact of self-heating on the statistical variability of bulk FinFETs. 3D electro-thermal simulations have been performed using the GSS statistical-variability-aware device simulator GARAND, recently enhanced with a thermal simulation module. A bulk FinFET, designed to meet the specifications for the 14/16nm CMOS Technology Generation, is used as a test bed, taking into account the combined effects of gate edge roughness, fin edge roughness and metal gate granularity. The statistical distribution of key figures of merit, especially the on-current, under the influence of thermal effects, is analysed.
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FinFET Centric Variability-Aware Compact Model Extraction and Generation Technology Supporting DTCO
IEEE Transactions on Electron Devices, 2015Co-Authors: Xingsheng Wang, Binjie Cheng, C Millar, David Reid, Andrew Pender, Plamen Asenov, A AsenovAbstract:In this paper, we present a FinFET-focused variability-aware compact model (CM) extraction and Generation Technology supporting design-Technology co-optimization. The 14-nm CMOS Technology Generation silicon on insulator FinFETs are used as testbed transistors to illustrate our approach. The TCAD simulations include a long-range process-induced variability using a design of experiment approach and short-range purely statistical variability (mismatch). The CM extraction supports a hierarchical CM approach, including nominal CM extraction, response surface CM extraction, and statistical CM extraction. The accurate CM Generation Technology captures the often non-Gaussian distributions of the key transistor figures of merit and their correlations preserving also the correlations between process and statistical variability. The use of the hierarchical CM is illustrated in the simulation of FinFET-based SRAM cells and ring oscillators.
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Impact of NBTI/PBTI on SRAM Stability Degradation
IEEE Electron Device Letters, 2011Co-Authors: Binjie Cheng, Andrew R. Brown, A AsenovAbstract:We investigate the impact of negative-bias temperature instability (NBTI) on the degradation of the static noise margin (SNM) and write noise margin (WNM) of a SRAM cell. This is based on the quantitative simulation of the statistical impact of NBTI on p-MOSFETs corresponding to a 45-nm low-power Technology Generation. Due to the increasing importance of positive-bias temperature instability (PBTI) of n-MOSFETs with the introduction of high-/v/metal gate stacks, we also explore the additional impact of PBTI on statistical SNM and WNM degradation behavior. The results indicate that NBTI-only induced SNM and WNM degradations follow different evolutionary patterns compared to the impact of simultaneous NBTI and PBTI degradation, and high distribution moment information is required for the reconstruction of noise margin distributions.
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Evaluation of statistical variability in 32 and 22 nm Technology Generation LSTP MOSFETs
2009Co-Authors: Binjie Cheng, C Millar, Andrew R. Brown, Scott Roy, A AsenovAbstract:The quantitative evaluation of the impact of key sources of static and dynamic statistical variability (SV) are presented for LSTP nMOSFETs corresponding to 32 nm and 22 nm Technology Generation transistors with thin-body (TB) SOI and double gate (DG) architectures, respectively. The simulation results indicate that TB SOI and DG devices are not only more resistant to random dopant induced variability compared to their bulk counterparts, but are also more tolerant to line edge roughness induced variability. However, the improved static SV performance shifts the emphasis to dynamic SV introduced by trapped charge associated with aging processes.
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evaluation of intrinsic parameter fluctuations on 45 32 and 22nm Technology node lp n mosfets
European Solid-State Device Research Conference, 2008Co-Authors: Binjie Cheng, A R Brown, S. Roy, C Millar, A AsenovAbstract:The quantitative evaluation of the impact of key sources of statistical variability (SV) are presented for LP nMOSFETs corresponding to 45 nm, 32 nm and 22 nm Technology Generation transistors with bulk, thin body (TB) SOI and double gate (DG) device architectures respectively. The simulation results indicate that TBSOI and DG are not only resistant to random dopant induced variability, but also are more tolerant to line edge roughness induced variability. Even two Technology Generations ahead from their bulk counterparts, DG MOSFETs will still have 4 times less variability than bulk devices.
Kar Yan Tam - One of the best experts on this subject based on the ideXlab platform.
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Winning Back Technology Disadopters: Testing a Technology Re-Adoption Model in the Context of Mobile Internet Services
2017Co-Authors: James Y.l. Thong, Kar Yan TamAbstract:This paper addresses the issue of how Information and Communication Technology (ICT) service providers can win back disadopters of an earlier Generation of Technology when a new Technology Generation appears on the market. Integrating prior research on consumers’ defensive bias, knowledge accessibility, diffusion of innovation, and Technology adoption, we developed a model to predict disadopters’ intention to re-adopt a Technology. We postulate that the primary reason for disadoption moderates the impacts of both the drivers of re-adoption (i.e., perceived superiority, effort expectancy, price value of the new Technology Generation, and social influence) and the characteristics of prior usage experience with the disadopted earlier Technology Generation (i.e., duration of disadoption, tenure with the old Generation, and usage intensity of the old Generation) on re-adoption intention. We tested our Technology re-adoption model in the context of mobile Internet services. Data was collected from 274 disadopters of an earlier Generation of mobile Internet services before the advent of the third Generation (3G) Technology. The results supported most of our hypotheses. These findings have significant theoretical and practical implications, especially for firms interested in winning back Technology disadopters. Finally, we present an agenda for further research into Technology re-adoption.
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Winning Back Technology Disadopters: Testing a Technology Readoption Model in the Context of Mobile Internet Services
Journal of Management Information Systems, 2017Co-Authors: James Y.l. Thong, Kar Yan TamAbstract:AbstractWe investigate how information and communication Technology (ICT) service providers can win back disadopters of an earlier Generation of Technology when a new Technology Generation appears on the market. Integrating prior research on consumers’ defensive bias, knowledge accessibility, diffusion of innovation, and Technology adoption, we developed a model to predict disadopters’ intention to readopt a Technology. We postulate that the primary reason for disadoption moderates the impacts of both the drivers of readoption (perceived superiority, effort expectancy, price value of the new Technology Generation, and social influence) and the characteristics of prior usage experience with the disadopted earlier Technology Generation (duration of disadoption, tenure with the old Generation, and usage intensity of the old Generation) on readoption intention. We tested our Technology readoption model in the context of mobile Internet services. Data were collected from 274 disadopters of an earlier generatio...
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PACIS - Winning Back Technology Disadopters
2007Co-Authors: James Y.l. Thong, Kar Yan TamAbstract:This paper addresses the issue of winning back disadopters of an earlier Generation of Technology when a new Technology Generation appears on the market. Integrating research on innovation management, attitude strength and change, and consumer win-back, we propose a re-adoption model to predict disadopters’ intentions to come back. Data were collected from 274 disadopters of earlier mobile internet Generations facing the advent of 3G. We found that perceived superiority of a new Technology Generation, prior usage experience, and price value are significant enablers, and their influences are moderated by the reason for disadoption. These findings have significant managerial and theoretical implications.
Yoon-hwan Hahn - One of the best experts on this subject based on the ideXlab platform.
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Technological competition in network markets with policy implications
Technovation, 2004Co-Authors: Kwang-sun Lim, Yoon-hwan HahnAbstract:Abstract In this article, we study the technological competition in network markets—markets characterized by complementarities between and among economic agents. We identify three sources of complementarities and provide a working definition of network markets. Then, we proceed to discuss the Technology Generation and diffusion in network markets based upon traditional microeconomic and industrial organization theories of technological competition. Major findings are (1) network features of the market exacerbate distortion in private R&D investment, (2) Technology diffusion in network markets is vulnerable to manipulation from supply-side and to deviate from socially optimal diffusion path and (3) presence of network features is a prima facie rationale for synchronization of Technology Generation and diffusion policies. We conclude with implications for ongoing debate about intellectual property protection of software.
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Towards a new Technology policy: the integration of Generation and diffusion
Technovation, 1999Co-Authors: Yoon-hwan HahnAbstract:Abstract In this paper, we propose a novel framework for Technology policy aimed both for the Generation and diffusion of new technologies—diffusion-based incentive system (DBIS). Based upon the critical examination of related theories and the practice of Technology policy, we identify two major problems in current Technology policy: the ignorance of Technology diffusion policy relative to Technology Generation policy, and the segregated approach to Technology diffusion policy from Technology Generation policy. To remedy these problems, we argue that two goals of Technology policy of Generation and diffusion should be integrated and synchronized via a unified policy framework, and propose DBIS which can serve the desired objective.
James Y.l. Thong - One of the best experts on this subject based on the ideXlab platform.
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Winning Back Technology Disadopters: Testing a Technology Re-Adoption Model in the Context of Mobile Internet Services
2017Co-Authors: James Y.l. Thong, Kar Yan TamAbstract:This paper addresses the issue of how Information and Communication Technology (ICT) service providers can win back disadopters of an earlier Generation of Technology when a new Technology Generation appears on the market. Integrating prior research on consumers’ defensive bias, knowledge accessibility, diffusion of innovation, and Technology adoption, we developed a model to predict disadopters’ intention to re-adopt a Technology. We postulate that the primary reason for disadoption moderates the impacts of both the drivers of re-adoption (i.e., perceived superiority, effort expectancy, price value of the new Technology Generation, and social influence) and the characteristics of prior usage experience with the disadopted earlier Technology Generation (i.e., duration of disadoption, tenure with the old Generation, and usage intensity of the old Generation) on re-adoption intention. We tested our Technology re-adoption model in the context of mobile Internet services. Data was collected from 274 disadopters of an earlier Generation of mobile Internet services before the advent of the third Generation (3G) Technology. The results supported most of our hypotheses. These findings have significant theoretical and practical implications, especially for firms interested in winning back Technology disadopters. Finally, we present an agenda for further research into Technology re-adoption.
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Winning Back Technology Disadopters: Testing a Technology Readoption Model in the Context of Mobile Internet Services
Journal of Management Information Systems, 2017Co-Authors: James Y.l. Thong, Kar Yan TamAbstract:AbstractWe investigate how information and communication Technology (ICT) service providers can win back disadopters of an earlier Generation of Technology when a new Technology Generation appears on the market. Integrating prior research on consumers’ defensive bias, knowledge accessibility, diffusion of innovation, and Technology adoption, we developed a model to predict disadopters’ intention to readopt a Technology. We postulate that the primary reason for disadoption moderates the impacts of both the drivers of readoption (perceived superiority, effort expectancy, price value of the new Technology Generation, and social influence) and the characteristics of prior usage experience with the disadopted earlier Technology Generation (duration of disadoption, tenure with the old Generation, and usage intensity of the old Generation) on readoption intention. We tested our Technology readoption model in the context of mobile Internet services. Data were collected from 274 disadopters of an earlier generatio...
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PACIS - Winning Back Technology Disadopters
2007Co-Authors: James Y.l. Thong, Kar Yan TamAbstract:This paper addresses the issue of winning back disadopters of an earlier Generation of Technology when a new Technology Generation appears on the market. Integrating research on innovation management, attitude strength and change, and consumer win-back, we propose a re-adoption model to predict disadopters’ intentions to come back. Data were collected from 274 disadopters of earlier mobile internet Generations facing the advent of 3G. We found that perceived superiority of a new Technology Generation, prior usage experience, and price value are significant enablers, and their influences are moderated by the reason for disadoption. These findings have significant managerial and theoretical implications.
