She is currently working on domain dynamics, 2D ferroelectrics, and nonvolatile memories based on piezoresponse force microscopy. Ni Zhong received her B.S. degree from Shanghai Institute of Ceramics, Chinese Academy of Sciences, and her Ph.D. from NARA Institute of Science and Technology (NAIST), Japan. In 2012, she joined the Key Laboratory of Polar Materials and Devices, Ministry of Education, East China Normal University as an associate professor. She is currently focusing on ferroelectric thin films/2D ferroelectrics/strongly correlated material and novel devices for next-generation computing systems.

Memristors with history‐dependent resistance are considered as artificial synapses and have potential in mimicking the massive parallelism and low‐power operation existing in the human brain. However, the state‐of‐the‐art memristors still suffer from excessive write noise, abrupt resistance variation, inherent stochasticity, poor endurance behavior, and costly energy consumption, which impedes massive neural architecture. A robust and low‐energy consumption organic three‐terminal memristor based on ferroelectric polymer gate insulator is demonstrated here. The conductance of this memristor can be precisely manipulated to vary between more than 1000 intermediate states with the highest OFF/ON ratio of ≈104. The quasicontinuous resistive switching in the MoS2 channel results from the ferroelectric domain dynamics as confirmed unambiguously by the in situ real‐time correlation between dynamic resistive switching and polarization change. Typical synaptic plasticity such as long‐term potentiation and depression (LTP/D) and spike‐timing dependent plasticity (STDP) are successfully simulated. In addition, the device is expected to experience 1 × 109 synaptic spikes with an ultralow energy consumption for each synaptic operation (less than 1 fJ, compatible with a bio‐synaptic event), which highlights its immense potential for the massive neural architecture in bioinspired networks.

We demonstrate electrostatic control of the metal-insulator transition in the typical correlated-electron material NdNiO3 through a large effective capacitance of the electric double layer at the electrolyte/NdNiO3 interface. The metal-insulator transition temperature (TMI) of NdNiO3 is shown to decrease drastically with increasing hole concentration through the application of a negative gate voltage (VG). The shift in TMI (|ΔTMI|) is larger for thinner NdNiO3; for VG of −2.5 V, |ΔTMI| of 5-nm-thick NdNiO3 is as large as 40 K, and the resistivity change near 95 K is one order of magnitude. This study may be potentially applicable to Mott transistor devices.

This paper presents a novel optimizing compiler for general purpose computation on graphics processing units (GPGPU). It addresses two major challenges of developing high performance GPGPU programs: effective utilization of GPU memory hierarchy and judicious management of parallelism.The input to our compiler is a naïve GPU kernel function, which is functionally correct but without any consideration for performance optimization. The compiler analyzes the code, identifies its memory access patterns, and generates both the optimized kernel and the kernel invocation parameters. Our optimization process includes vectorization and memory coalescing for memory bandwidth enhancement, tiling and unrolling for data reuse and parallelism management, and thread block remapping or address-offset insertion for partition-camping elimination. The experiments on a set of scientific and media processing algorithms show that our optimized code achieves very high performance, either superior or very close to the highly fine-tuned library, NVIDIA CUBLAS 2.2, and up to 128 times speedups over the naive versions. Another distinguishing feature of our compiler is the understandability of the optimized code, which is useful for performance analysis and algorithm refinement.

A prototype Mott transistor, the electric double layer transistor with a strained CaMnO(3) thin film, is fabricated. As predicted by the strain phase diagram of electron-doped manganite films, the device with the compressively strained CaMnO(3) exhibits an immense conductivity modulation upon applying a tiny gate voltage of 2 V.

No prediction rule is currently available for advanced colorectal neoplasms, defined as invasive cancer, an adenoma of 10 mm or more, a villous adenoma, or an adenoma with high-grade dysplasia, in average-risk Chinese. In this study between 2006 and 2008, a total of 7,541 average-risk Chinese persons aged 40 years or older who had complete colonoscopy were included. The derivation and validation cohorts consisted of 5,229 and 2,312 persons, respectively. A prediction rule was developed from a logistic regression model and then internally and externally validated. The prediction rule comprised 8 variables (age, sex, smoking, diabetes mellitus, green vegetables, pickled food, fried food, and white meat), with scores ranging from 0 to 14. Among the participants with low-risk (≤3) or high-risk (>3) scores in the validation cohort, the risks of advanced neoplasms were 2.6% and 10.0% (P < 0.001), respectively. If colonoscopy was used only for persons with high risk, 80.3% of persons with advanced neoplasms would be detected while the number of colonoscopies would be reduced by 49.2%. The prediction rule had good discrimination (area under the receiver operating characteristic curve = 0.74, 95% confidence interval: 0.70, 0.78) and calibration (P = 0.77) and, thus, provides accurate risk stratification for advanced neoplasms in average-risk Chinese.

Designing transparent flexible electronics with multi‐biological neuronal functions and superior flexibility is a key step to establish wearable artificial intelligence equipment. Here, a flexible ionic gel‐gated VO2 Mott transistor is developed to simulate the functions of the biological synapse. Short‐term and long‐term plasticity of the synapse are realized by the volatile electrostatic carrier accumulation and nonvolatile proton‐doping modulation, respectively. With the achievement of multi‐essential synaptic functions, an important sensory neuron, nociceptor, is perfectly simulated in our synaptic transistors with all key characteristics of threshold, relaxation, and sensitization. More importantly, this synaptic transistor exhibits high tolerance to the bending deformation, and the cycle‐to‐cycle variations of multi‐conductance states in potentiation and depression properties are maintained within 4%. This superior stability further indicates that our flexible device is suitable for neuromorphic computing. Simulation results demonstrate that high recognition accuracy of handwritten digits (>95%) can be achieved in a convolution neural network built from these synaptic transistors. The transparent and flexible Mott transistor based on electrically‐controlled VO2 metal‐insulator transition is believed to open up alternative approaches to developing highly stable synapses for future flexible neuromorphic systems.

This paper presents a new modeling and simulation method to predict the important statistical performance of single photon avalanche diode (SPAD) detectors, including photon detection efficiency (PDE), dark count rate (DCR) and afterpulsing probability (AP). Three local electric field models are derived for the PDE, DCR and AP calculations, which show analytical dependence of key parameters such as avalanche triggering probability, impact ionization rate and electric field distributions that can be directly obtained from Geiger mode Technology Computer Aided Design (TCAD) simulation. The model calculation results are proven to be in good agreement with the reported experimental data in the open literature, suggesting that the proposed modeling and simulation method is very suitable for the prediction of SPAD statistical performance.