arXiv subjects

Yitong Chen

Publications and source records attributed to Yitong Chen.

40 records · Page 3Linked to original sources

arXiv2023

Two-dimensional heavy fermion in a monoatomic-layer Kondo lattice YbCu$_2$

The Kondo effect between localized $f$-electrons and conductive carriers leads to exotic physical phenomena. Among them, heavy-fermion (HF) systems, in which massive effective carriers appear due to the Kondo effect, have fascinated many researchers. Dimensionality is also an important characteristic of the HF system, especially because it is strongly related to quantum criticality [S. Sachdev, Science 288, 475 (2000)]. However, perfect two-dimensional (2D) HF materials have not been reported yet. Here, we report the surface electronic structure of the monoatomic-layer Kondo lattice YbCu$_2$ on a Cu(111) surface observed by synchrotron-based angle-resolved photoelectron spectroscopy. The 2D conducting band and the Yb 4$f$ state, located very close to the Fermi level, are observed. These bands are hybridized at low-temperature, forming the 2D HF state, with an evaluated coherent temperature of about 30 K. The effective mass of the 2D state is enhanced by a factor of 100 by the development of the HF state. Furthermore, clear evidence of the hybridization gap formation in the temperature dependence of the Kondo-resonance peak has been observed below the coherent temperature. Our study provides a new candidate as an ideal 2D HF material for understanding the Kondo effect at low dimensions.

cond-mat.str-el↗

arXiv2022

Photo-induced nonlinear band shift and valence transition in SmS

The photo-induced band structure variation of a rare-earth-based semiconductor, samarium monosulfide (SmS), was investigated using high-harmonic-generation laser-based time-resolved photoelectron spectroscopy. A nonlinear photo-induced band shift of the Sm 4f multiplets was observed. The first one is a shift to the high-binding-energy side due to a large surface photovoltage (SPV) effect of approximately 93 meV, comparable to the size of the bulk band gap, with a much longer relaxation time than 0.1 ms. The second one is an ultrafast band shift to the low binding energy side, which is in the opposite direction to the SPV shift, suggesting an ultrafast valence transition from divalent to trivalent Sm ions due to photo-excitation. The latter energy shift was approximately 58 meV, which is consistent with the energy gap shift from ambient pressure to the boundary between the black insulator and golden metallic phase with the application of pressure. This suggests that the photo-induced valence transition can reach the phase boundary, but other effects are necessary to realize the golden metallic phase.

cond-mat.mtrl-sci↗

arXiv2022

Photonic unsupervised learning processor for secure and high-throughput optical fiber communication

Following the explosive growth of global data, there is an ever-increasing demand for high-throughput optical fiber communication (OFC) systems to perform massive data transmission and processing. Existing OFC methods mainly rely on electronic circuits for data processing, which severely limits the communication throughput. Though considered promising for the next-generation high-speed fiber communication, all-optical OFC remains unachievable due to serious challenges in effective optical computing, system modeling and configuring. Here we propose an end-to-end photonic encoder-decoder (PED) processor which maps the physical system of OFC into an optical generative neural network. By modeling the OFC transmission process as the variation in the constructed optical latent space, the PED learns noise-resistant coding schemes via unsupervised optimization. With multi-layer parametric diffractive neural networks, the PED establishes a large-scale and high-throughput optical computing framework that integrates the main OFC computations including coding, encryption and compression to the optical domain. The whole system improves the latency of computation in OFC systems by five orders of magnitude compared with the state-of-the-art device. On benchmarking datasets, the PED experimentally achieves up to 32% reduction in transmission error ratio (ER) than on-off keying (OOK), one of the mainstream methods with the lowest ER in general transmission. As we demonstrate on medical data, the PED increases the transmission throughput by two orders of magnitude than 8-level pulse amplitude modulation (PAM-8). We believe the proposed photonic encoder-decoder processor not only paves the way to the next-generation all-optical OFC systems, but also promotes a wide range of AI-based physical system designs.

physics.optics↗

arXiv2020

Large-scale neuromorphic optoelectronic computing with a reconfigurable diffractive processing unit

Application-specific optical processors have been considered disruptive technologies for modern computing that can fundamentally accelerate the development of artificial intelligence (AI) by offering substantially improved computing performance. Recent advancements in optical neural network architectures for neural information processing have been applied to perform various machine learning tasks. However, the existing architectures have limited complexity and performance; and each of them requires its own dedicated design that cannot be reconfigured to switch between different neural network models for different applications after deployment. Here, we propose an optoelectronic reconfigurable computing paradigm by constructing a diffractive processing unit (DPU) that can efficiently support different neural networks and achieve a high model complexity with millions of neurons. It allocates almost all of its computational operations optically and achieves extremely high speed of data modulation and large-scale network parameter updating by dynamically programming optical modulators and photodetectors. We demonstrated the reconfiguration of the DPU to implement various diffractive feedforward and recurrent neural networks and developed a novel adaptive training approach to circumvent the system imperfections. We applied the trained networks for high-speed classifying of handwritten digit images and human action videos over benchmark datasets, and the experimental results revealed a comparable classification accuracy to the electronic computing approaches. Furthermore, our prototype system built with off-the-shelf optoelectronic components surpasses the performance of state-of-the-art graphics processing units (GPUs) by several times on computing speed and more than an order of magnitude on system energy efficiency.

eess.IV↗