Related papers: Photonic non-volatile memories using phase change …
We experimentally demonstrate waveguide and microring resonator (MRR) polarizers by integrating 2D graphene oxide (GO) films onto silicon (Si) photonic devices. The 2D GO films with highly anisotropic light absorption are on-chip integrated…
We demonstrate an all solid state semiconductor device, based on epitaxial single crystalline metal halide perovskites, enabling reversible control of a perovskite photoluminescence with a gate voltage. Fundamentally distinct from…
Memory cells are an important building block of digital electronics. We combine here the unique electronic properties of semiconducting monolayer MoS2 with the high conductivity of graphene to build a 2D heterostructure capable of…
The magnetoelectric effects in multiferroics have a great potential in creating next-generation memory devices. We conceive a new concept of non-volatile memories based on a type of nonlinear magnetoelectric effects showing a…
This article proposes a concept for dynamically reconfigurable distributed microwave circuits by leveraging the abrupt conductivity transition in phase-change materials (PCM). Metallic surface-inclusions (<<$\lambda$) are embedded in the…
The stability of the integrated photonic circuits is of critical importance for many applications that require high frequency precision or robust operation over time, such as optomechanical sensing, frequency conversion, optical…
The Kerr nonlinearity can be a key enabler for many digital photonic circuits as it allows access to bistable states needed for all-optical memories and switches. A common technique is to use the Kerr shift to control the resonance…
Photonic integrated circuits (PICs) demand tailored spectral responses for various applications. On-chip Bragg filters offer a promising solution, yet their static nature hampers scalability. Current tunable filters rely on volatile…
Memristive devices have drawn considerable research attention due to their potential applications in non-volatile memory and neuromorphic computing. The combination of resistive switching devices with light-responsive materials is…
The reversible phase transition between orthorhombic and cubic phases is demonstrated in all-inorganic perovskite CsPbIBr2 grown by a dual-source vapor-deposition method. The phase transition is actuated via heating and moisture exposure.…
Efficient conversion of electrical signal to optical signal in nano-photonics enables solid state integration of electronics and photonics. Combination of graphene with photonic crystals is promising for electro-optic modulation. In this…
We propose a novel topological photonic memory that encodes information through dynamically controllable Chern numbers in a two-band topological photonic system. Utilizing a honeycomb lattice photonic crystal, the memory leverages…
We introduce non-Hermitian plasmonic waveguide-cavity structures based on the Aubry-Andre-Harper model to realize switching between right and left topological edge states (TESs) using the phase-change material Ge$_2$Sb$_2$Te$_5$ (GST). We…
Efficient nonlinear frequency conversion in nanophotonics requires not only strong fundamental field but also precise phase matching among distributed nonlinear sources. Here, we develop the two-dimensional Green's function integral method…
The synergy between metasurfaces and non-volatile phase change materials (PCMs) has created many reconfigurable photonic devices for applications in optical memory, optical computing and optical communications. But these advances have been…
Plastic self-adaptation, nonlinear recurrent dynamics and multi-scale memory are desired features in hardware implementations of neural networks, because they enable them to learn, adapt and process information similarly to the way…
Resistive switching memories allow electrical control of the conductivity of a material, by inducing a high resistance (OFF) or a low resistance (ON) state, using electrochemical and ion transport processes. As alternative to high…
On-chip photonic processors for neural networks have potential benefits in both speed and energy efficiency but have not yet reached the scale at which they can outperform electronic processors. The dominant paradigm for designing on-chip…
With a rapidly growing amount of data generated and processed, a search for more efficient components and architectures such as neuromorphic computing that are able to perform a more and more complex operations in more efficient way…
Reconfigurable memristors featuring neural and synaptic functions hold great potential for neuromorphic circuits by simplifying system architecture, cutting power consumption, and boosting computational efficiency. Their additive…