scholarly journals A scheme for simulating multi-level phase change photonics materials

2021 ◽  
Vol 7 (1) ◽  
Author(s):  
Yunzheng Wang ◽  
Jing Ning ◽  
Li Lu ◽  
Michel Bosman ◽  
Robert E. Simpson
Keyword(s):  
Neural Networks ◽  
Phase Transitions ◽  
Phase Change ◽  
Phase Change Materials ◽  
Effective Medium Theory ◽  
Optical Response ◽  
Nanosecond Laser ◽  
Optical Neural Networks ◽  
All Optical ◽  
Multi Level

AbstractChalcogenide phase change materials (PCMs) have been extensively applied in data storage, and they are now being proposed for high resolution displays, holographic displays, reprogrammable photonics, and all-optical neural networks. These wide-ranging applications all exploit the radical property contrast between the PCMs’ different structural phases, extremely fast switching speed, long-term stability, and low energy consumption. Designing PCM photonic devices requires an accurate model to predict the response of the device during phase transitions. Here, we describe an approach that accurately predicts the microstructure and optical response of phase change materials during laser induced heating. The framework couples the Gillespie Cellular Automata approach for modelling phase transitions with effective medium theory and Fresnel equations. The accuracy of the approach is verified by comparing the PCM’s optical response and microstructure evolution with the results of nanosecond laser switching experiments. We anticipate that this approach to simulating the switching response of PCMs will become an important component for designing and simulating programmable photonics devices. The method is particularly important for predicting the multi-level optical response of PCMs, which is important for all-optical neural networks and PCM-programmable perceptrons.

scholarly journals Programmable phase-change metasurfaces on waveguides for multimode photonic convolutional neural network

2021 ◽  
Vol 12 (1) ◽  
Author(s):  
Changming Wu ◽  
Heshan Yu ◽  
Seokhyeong Lee ◽  
Ruoming Peng ◽  
Ichiro Takeuchi ◽  
...  
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Phase Change ◽  
Convolutional Neural Network ◽  
Large Scale ◽  
Phase Change Materials ◽  
Refractive Index Change ◽  
Optical Computing ◽  
Machine Learning Algorithms ◽  
Matrix Vector Multiplication

AbstractNeuromorphic photonics has recently emerged as a promising hardware accelerator, with significant potential speed and energy advantages over digital electronics for machine learning algorithms, such as neural networks of various types. Integrated photonic networks are particularly powerful in performing analog computing of matrix-vector multiplication (MVM) as they afford unparalleled speed and bandwidth density for data transmission. Incorporating nonvolatile phase-change materials in integrated photonic devices enables indispensable programming and in-memory computing capabilities for on-chip optical computing. Here, we demonstrate a multimode photonic computing core consisting of an array of programable mode converters based on on-waveguide metasurfaces made of phase-change materials. The programmable converters utilize the refractive index change of the phase-change material Ge2Sb2Te5 during phase transition to control the waveguide spatial modes with a very high precision of up to 64 levels in modal contrast. This contrast is used to represent the matrix elements, with 6-bit resolution and both positive and negative values, to perform MVM computation in neural network algorithms. We demonstrate a prototypical optical convolutional neural network that can perform image processing and recognition tasks with high accuracy. With a broad operation bandwidth and a compact device footprint, the demonstrated multimode photonic core is promising toward large-scale photonic neural networks with ultrahigh computation throughputs.

Using Neural Networks or Linear Models to Predict the Characteristics of Microcapsules Containing Phase Change Materials

2010 ◽  
Vol 287 (1) ◽  
pp. 162-167 ◽  
Author(s):  
Luz Sánchez ◽  
Paula Sánchez ◽  
Antonio De Lucas ◽  
Manuel Carmona ◽  
Juan Francisco Rodríguez
Keyword(s):  
Neural Networks ◽  
Phase Change ◽  
Phase Change Materials ◽  
Linear Models

A New Optical Neuron Device for All-Optical Neural Networks

1991 ◽  
Author(s):  
Akio Takimoto ◽  
Koji Akiyama ◽  
Michihiro Miyauchi ◽  
Yasunori Kuratomi ◽  
Junko Asayama ◽  
...  
Keyword(s):  
Neural Networks ◽  
Optical Neural Networks ◽  
All Optical

scholarly journals All-Chalcogenide Programmable All-Optical Deep Neural Networks

2021 ◽  
Author(s):  
Ting Yu ◽  
Xiaoxuan Ma ◽  
Ernest Pastor ◽  
Jonathan George ◽  
Simon Wall ◽  
...  
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Autonomous Vehicles ◽  
Structural Phase ◽  
Activation Function ◽  
Navigation Systems ◽  
Optical Neural Networks ◽  
Electro Optic ◽  
All Optical ◽  
On Chip

Abstract Deeplearning algorithms are revolutionising many aspects of modern life. Typically, they are implemented in CMOS-based hardware with severely limited memory access times and inefficient data-routing. All-optical neural networks without any electro-optic conversions could alleviate these shortcomings. However, an all-optical nonlinear activation function, which is a vital building block for optical neural networks, needs to be developed efficiently on-chip. Here, we introduce and demonstrate both optical synapse weighting and all-optical nonlinear thresholding using two different effects in one single chalcogenide material. We show how the structural phase transitions in a wide-bandgap phase-change material enables storing the neural network weights via non-volatile photonic memory, whilst resonant bond destabilisation is used as a nonlinear activation threshold without changing the material. These two different transitions within chalcogenides enable programmable neural networks with near-zero static power consumption once trained, in addition to picosecond delays performing inference tasks not limited by wire charging that limit electrical circuits; for instance, we show that nanosecond-order weight programming and near-instantaneous weight updates enable accurate inference tasks within 20 picoseconds in a 3-layer all-optical neural network. Optical neural networks that bypass electro-optic conversion altogether hold promise for network-edge machine learning applications where decision-making in real-time are critical, such as for autonomous vehicles or navigation systems such as signal pre-processing of LIDAR systems.

Studies of Ge-Sb-Te Phase Change Materials At and Above Melting Temperatures and Set to Reset Transition of Memory Devices

2010 ◽  
Vol 1251 ◽  
Author(s):  
Semyon D Savransky ◽  
Guy Wicker
Keyword(s):  
Phase Change ◽  
Phase Change Materials ◽  
Memory Devices ◽  
Melting Temperatures ◽  
Electrical Studies ◽  
Melting Region ◽  
Electrical Resistivities ◽  
Multi Level ◽  
Selection For ◽  
Change Memory

AbstractThe results of calorimetric and electrical studies of bulk Ge2Sb2Te5 and GeSb2Te4 alloys around melting temperature Tm are presented together with characteristics of phase-change memory devices from such alloys. The endothermic melting region is wider in Ge2Sb2Te5 than GeSb2Te4. Electrical resistivities of the alloys in this region have semiconductor characteristics. The width of the melting region correlates with breadth of set to reset transition in devices. This empirical correlation is probably important for alloy selection for multi-level memory cells.

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