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Abstract A new concept of read-out method for ferroelectric random-access memory (FeRAM) using a graphene layer as the channel material of bottom-gated field effect transistor structure is demonstrated experimentally. The transconductance of the graphene channel is found to change its sign depending on the direction of spontaneous polarization (SP) in the underlying ferroelectric layer. This indicates that the memory state of FeRAM, specified by the SP direction of the ferroelectric layer, can be sensed unambiguously with transconductance measurements. With the proposed read-out method, it is possible to construct an array of ferroelectric memory cells in the form of a cross-point structure where the transconductance of a crossing cell can be measured selectively without any additional selector. This type of FeRAM can be a plausible solution for fabricating high speed, ultra-low power, long lifetime, and high density 3D stackable non-volatile memory.

In recent years, fade detection algorithms can classify fade scenes in massive video libraries have been developed. However, these algorithms misclassify some non-fade scenes as fade scenes, especially dissolve scenes and scenes with captions or flashing light sources. This paper proposes a new fade detection algorithm that uses similarity tendencies of luminance transitions to overcome such obstacles. To prevent detection accuracy degradation by letterboxing and captions, video frames are simplified. Then, fade candidates are detected by transition boundary detection using the angular and curvature characteristics of the luminance vectors. Finally, luminance flipping detection improves the detection accuracy by extracting the luminance retrograde phenomenon that occurs with flashing or light source movements. Through objective evaluation using $F_{1}$ score, the detection accuracy of the proposed algorithm was 0.884, which is an increase of 0.187 (21.2% improvement) compared with the average $F_{1}$ score of existing high-performance methods.

Modern mobile systems are requested to execute diverse user scenarios. Depending on the types of user scenarios, mobile systems utilize hardware resources differently. Thus, power management policies of mobile systems must adapt to various user scenarios. In this paper, we propose a dynamic voltage/frequency scaling (DVFS) policy to increase the energy efficiency of multicore mobile systems by adapting to user scenarios. The proposed policy provides effective power management regardless of user scenarios by using operation characteristics that can represent the execution behavior of various user scenarios. Furthermore, the proposed policy is suitable for modern mobile systems in which online power management is essential, because it does not require preliminary knowledge of target scenarios. To balance the trade-off between energy consumption and quality-of-service (QoS), the proposed scenario-aware policy provides `just enough' processing speed to process the requested amount of work at the given parallelism level. To demonstrate the practicality of the proposed policy, we evaluated the effectiveness of the proposed scenario-aware policy for real-world user scenarios. Compared to the conventional DVFS policies, the proposed scenario-aware policy achieved a maximum of 25.5% energy saving on the mobile system that uses asymmetric multicore CPU, and a maximum of 30.7% energy saving on the mobile system that uses symmetric multicore CPU, without any QoS violation that degrades user experiences.

We propose SmartGrid, a new video-retargeting method that preserves a shape of the salient object and maintains a temporal coherence for the static background regions in the video. Previous methods mainly focus on preserving the shape of the salient object or maintaining the temporal coherence for each region. However, these methods do not explicitly adjust the sizes of the grids corresponding to the salient objects and the static background regions. Thus, they have difficulty in maintaining the consistency of the salient object shape and the temporal coherence for the static background regions. The basic idea of SmartGrid is to maintain the consistency of the contents in consecutive frames by analyzing the degree of the spatiotemporal consistency. Compared to the best results obtained using eight previous methods, SmartGrid achieved improvements of 1.19× in Bidirectional Similarity Measure, 7.59× in Jittery Metric 1, and 13.16× in Jittery Metric 2, and reduced the average computation time per pixel by 6.14×.

With the increasing demand for state-of-the-art technologies, such as wearable devices and the Internet of things (IoT), power integrity has emerged as a major concern for high-speed, low-power interfaces that are used as mobile platforms. By using case-specific design models in a high-speed memory system, only a limited analysis of the effects of parametric variations can be performed in complex design problems, such as adjacent voltage domain coupling at high frequencies. Moreover, a conventional industrial method can be simulated only after completing the design layout; therefore, a number of iterative back-annotation processes are required for signoff; this delays the time to market. In this paper, we propose a power integrity coanalysis methodology for multiple power domains in high-frequency memory systems. Our proposed methodology can analyze the tendencies in power integrity by using parametric methods, such as parameter sweeping and Monte Carlo simulations. Our experiments prove that our proposed methodology can predict similar peak-to-peak ripple voltages that are comparable with the realistic simulations of low-power double data rate four interfaces.

In the design process of advanced semiconductor devices, statistical leakage analysis has emerged as a major step due to uncertainties in the leakage current caused by the process variations. In this paper, a novel statistical leakage analysis which uses Gaussian mixture model (GMM) as the density function of leakage current is proposed. To estimate the probability density function, our proposed method clusters the rapidly converged leakage data using the GMM. The GMM can represent any distributions, soit is suitable to estimate the leakage distribution, which varies as the technology node or operating condition changes. In addition, our proposed method (SLA-GMM) defines a terminating condition that guarantees the convergence of the leakage data and prevents the underfitting or overfitting in the GMM modeling process. With sequential addition, SLA-GMM significantly reduced the error that can occur during the addition process. In studies with a goodness-of-fit test, SLA-GMM achieved up to 98% and 94% improvements in the Chi-square static and the K-S static compared with the previous method based on an analytic model.

We propose SoftCorner, which is a novel approach to estimate the worst delay (3σ delay) of a VLSI system. SoftCorner is a modification of deterministic static timing analysis to overcome its tendency to give pessimistic estimates of the system delays when it uses 3σ corner delays for logic gates. The basic idea of SoftCorner is to use corner delays that are relaxed to th, 95th and 85th percentiles with an average error 4 times faster than the MC simulation on average.