As an industry accepted storage scheme, hafnium oxide（HfO_x） based resistive random access memory（RRAM）should further improve its thermal stability and data retention for practical applications. We therefore fabri...As an industry accepted storage scheme, hafnium oxide（HfO_x） based resistive random access memory（RRAM）should further improve its thermal stability and data retention for practical applications. We therefore fabricated RRAMs with HfO_x/ZnO double-layer as the storage medium to study their thermal stability as well as data retention. The HfO_x/ZnO double-layer is capable of reversible bipolar switching under ultralow switching current（〈 3 μA） with a Schottky emission dominant conduction for the high resistance state and a Poole–Frenkel emission governed conduction for the low resistance state. Compared with a drastically increased switching current at 120℃ for the single HfO_x layer RRAM, the HfO_x/ZnO double-layer exhibits excellent thermal stability and maintains neglectful fluctuations in switching current at high temperatures（up to 180℃）, which might be attributed to the increased Schottky barrier height to suppress current at high temperatures. Additionally, the HfO_x/ZnO double-layer exhibits 10-year data retention @85℃ that is helpful for the practical applications in RRAMs.展开更多
基于300 mm 0.18μm MS 5 V工艺平台设计并流片了1k×16一次性可编程OTP器件,并对存储单元的结构、工作原理及工艺等可能影响数据保持寿命的因素进行了分析。根据Arrhenius寿命模型对不同样品设置了高温老化实验测试,收集数据并对OT...基于300 mm 0.18μm MS 5 V工艺平台设计并流片了1k×16一次性可编程OTP器件,并对存储单元的结构、工作原理及工艺等可能影响数据保持寿命的因素进行了分析。根据Arrhenius寿命模型对不同样品设置了高温老化实验测试,收集数据并对OTP器件的保持特性进行建模。通过225℃、250℃和275℃条件下的高温老化加速实验,拟合样品最大数据保持时间曲线。在生产过程中可能出现的最差产品条件下,对1/(kT)与数据保持时间曲线进行数学拟合,计算在不同失效条件下的浮栅电荷泄漏的激活能和最大数据保持时间。展开更多
A differentiable neural computer(DNC)is analogous to the Von Neumann machine with a neural network controller that interacts with an external memory through an attention mechanism.Such DNC’s offer a generalized metho...A differentiable neural computer(DNC)is analogous to the Von Neumann machine with a neural network controller that interacts with an external memory through an attention mechanism.Such DNC’s offer a generalized method for task-specific deep learning models and have demonstrated reliability with reasoning problems.In this study,we apply a DNC to a language model(LM)task.The LM task is one of the reasoning problems,because it can predict the next word using the previous word sequence.However,memory deallocation is a problem in DNCs as some information unrelated to the input sequence is not allocated and remains in the external memory,which degrades performance.Therefore,we propose a forget gatebased memory deallocation(FMD)method,which searches for the minimum value of elements in a forget gate-based retention vector.The forget gatebased retention vector indicates the retention degree of information stored in each external memory address.In experiments,we applied our proposed NTM architecture to LM tasks as a task-specific example and to rescoring for speech recognition as a general-purpose example.For LM tasks,we evaluated DNC using the Penn Treebank and enwik8 LM tasks.Although it does not yield SOTA results in LM tasks,the FMD method exhibits relatively improved performance compared with DNC in terms of bits-per-character.For the speech recognition rescoring tasks,FMD again showed a relative improvement using the LibriSpeech data in terms of word error rate.展开更多
基金supported by the National Natural Science Foundation of China(Grant Nos.61006003 and 61674038)the Natural Science Foundation of Fujian Province,China(Grant Nos.2015J01249 and 2010J05134)+1 种基金the Science Foundation of Fujian Education Department of China(Grant No.JAT160073)the Science Foundation of Fujian Provincial Economic and Information Technology Commission of China(Grant No.83016006)
文摘As an industry accepted storage scheme, hafnium oxide（HfO_x） based resistive random access memory（RRAM）should further improve its thermal stability and data retention for practical applications. We therefore fabricated RRAMs with HfO_x/ZnO double-layer as the storage medium to study their thermal stability as well as data retention. The HfO_x/ZnO double-layer is capable of reversible bipolar switching under ultralow switching current（〈 3 μA） with a Schottky emission dominant conduction for the high resistance state and a Poole–Frenkel emission governed conduction for the low resistance state. Compared with a drastically increased switching current at 120℃ for the single HfO_x layer RRAM, the HfO_x/ZnO double-layer exhibits excellent thermal stability and maintains neglectful fluctuations in switching current at high temperatures（up to 180℃）, which might be attributed to the increased Schottky barrier height to suppress current at high temperatures. Additionally, the HfO_x/ZnO double-layer exhibits 10-year data retention @85℃ that is helpful for the practical applications in RRAMs.
文摘基于300 mm 0.18μm MS 5 V工艺平台设计并流片了1k×16一次性可编程OTP器件,并对存储单元的结构、工作原理及工艺等可能影响数据保持寿命的因素进行了分析。根据Arrhenius寿命模型对不同样品设置了高温老化实验测试,收集数据并对OTP器件的保持特性进行建模。通过225℃、250℃和275℃条件下的高温老化加速实验,拟合样品最大数据保持时间曲线。在生产过程中可能出现的最差产品条件下,对1/(kT)与数据保持时间曲线进行数学拟合,计算在不同失效条件下的浮栅电荷泄漏的激活能和最大数据保持时间。
基金supported by the ICT R&D By the Institute for Information&communications Technology Promotion(IITP)grant funded by the Korea government(MSIT)[Project Number:2020-0-00113,Project Name:Development of data augmentation technology by using heterogeneous information and data fusions].
文摘A differentiable neural computer(DNC)is analogous to the Von Neumann machine with a neural network controller that interacts with an external memory through an attention mechanism.Such DNC’s offer a generalized method for task-specific deep learning models and have demonstrated reliability with reasoning problems.In this study,we apply a DNC to a language model(LM)task.The LM task is one of the reasoning problems,because it can predict the next word using the previous word sequence.However,memory deallocation is a problem in DNCs as some information unrelated to the input sequence is not allocated and remains in the external memory,which degrades performance.Therefore,we propose a forget gatebased memory deallocation(FMD)method,which searches for the minimum value of elements in a forget gate-based retention vector.The forget gatebased retention vector indicates the retention degree of information stored in each external memory address.In experiments,we applied our proposed NTM architecture to LM tasks as a task-specific example and to rescoring for speech recognition as a general-purpose example.For LM tasks,we evaluated DNC using the Penn Treebank and enwik8 LM tasks.Although it does not yield SOTA results in LM tasks,the FMD method exhibits relatively improved performance compared with DNC in terms of bits-per-character.For the speech recognition rescoring tasks,FMD again showed a relative improvement using the LibriSpeech data in terms of word error rate.
