16 research outputs found
An All-in-One Bioinspired Neural Network
In
spite of recent advancements in artificial neural
networks (ANNs),
the energy efficiency, multifunctionality, adaptability, and integrated
nature of biological neural networks remain largely unimitated by
hardware neuromorphic computing systems. Here, we exploit optoelectronic,
computing, and programmable memory devices based on emerging two-dimensional
(2D) layered materials such as MoS2 to demonstrate a monolithically
integrated, multipixel, and āall-in-oneā bioinspired
neural network (BNN) capable of sensing, encoding, learning, forgetting,
and inferring at minuscule energy expenditure. We also demonstrate
learning adaptability and simulate learning challenges under specific
synaptic conditions to mimic biological learning. Our findings highlight
the potential of in-memory computing and sensing based on emerging
2D materials, devices, and integrated circuits to not only overcome
the bottleneck of von Neumann computing in conventional CMOS designs
but also to aid in eliminating the peripheral components necessary
for competing technologies such as memristors
An All-in-One Bioinspired Neural Network
In
spite of recent advancements in artificial neural
networks (ANNs),
the energy efficiency, multifunctionality, adaptability, and integrated
nature of biological neural networks remain largely unimitated by
hardware neuromorphic computing systems. Here, we exploit optoelectronic,
computing, and programmable memory devices based on emerging two-dimensional
(2D) layered materials such as MoS2 to demonstrate a monolithically
integrated, multipixel, and āall-in-oneā bioinspired
neural network (BNN) capable of sensing, encoding, learning, forgetting,
and inferring at minuscule energy expenditure. We also demonstrate
learning adaptability and simulate learning challenges under specific
synaptic conditions to mimic biological learning. Our findings highlight
the potential of in-memory computing and sensing based on emerging
2D materials, devices, and integrated circuits to not only overcome
the bottleneck of von Neumann computing in conventional CMOS designs
but also to aid in eliminating the peripheral components necessary
for competing technologies such as memristors
An All-in-One Bioinspired Neural Network
In
spite of recent advancements in artificial neural
networks (ANNs),
the energy efficiency, multifunctionality, adaptability, and integrated
nature of biological neural networks remain largely unimitated by
hardware neuromorphic computing systems. Here, we exploit optoelectronic,
computing, and programmable memory devices based on emerging two-dimensional
(2D) layered materials such as MoS2 to demonstrate a monolithically
integrated, multipixel, and āall-in-oneā bioinspired
neural network (BNN) capable of sensing, encoding, learning, forgetting,
and inferring at minuscule energy expenditure. We also demonstrate
learning adaptability and simulate learning challenges under specific
synaptic conditions to mimic biological learning. Our findings highlight
the potential of in-memory computing and sensing based on emerging
2D materials, devices, and integrated circuits to not only overcome
the bottleneck of von Neumann computing in conventional CMOS designs
but also to aid in eliminating the peripheral components necessary
for competing technologies such as memristors
An All-in-One Bioinspired Neural Network
In
spite of recent advancements in artificial neural
networks (ANNs),
the energy efficiency, multifunctionality, adaptability, and integrated
nature of biological neural networks remain largely unimitated by
hardware neuromorphic computing systems. Here, we exploit optoelectronic,
computing, and programmable memory devices based on emerging two-dimensional
(2D) layered materials such as MoS2 to demonstrate a monolithically
integrated, multipixel, and āall-in-oneā bioinspired
neural network (BNN) capable of sensing, encoding, learning, forgetting,
and inferring at minuscule energy expenditure. We also demonstrate
learning adaptability and simulate learning challenges under specific
synaptic conditions to mimic biological learning. Our findings highlight
the potential of in-memory computing and sensing based on emerging
2D materials, devices, and integrated circuits to not only overcome
the bottleneck of von Neumann computing in conventional CMOS designs
but also to aid in eliminating the peripheral components necessary
for competing technologies such as memristors
Ambipolar Phosphorene Field Effect Transistor
In this article, we demonstrate enhanced electron and hole transport in few-layer phosphorene field effect transistors (FETs) using titanium as the source/drain contact electrode and 20 nm SiO<sub>2</sub> as the back gate dielectric. The field effect mobility values were extracted to be ā¼38 cm<sup>2</sup>/Vs for electrons and ā¼172 cm<sup>2</sup>/Vs for the holes. On the basis of our experimental data, we also comprehensively discuss how the contact resistances arising due to the Schottky barriers at the source and the drain end effect the different regime of the device characteristics and ultimately limit the ON state performance. We also propose and implement a novel technique for extracting the transport gap as well as the Schottky barrier height at the metalāphosphorene contact interface from the ambipolar transfer characteristics of the phosphorene FETs. This robust technique is applicable to any ultrathin body semiconductor which demonstrates symmetric ambipolar conduction. Finally, we demonstrate a high gain, high noise margin, chemical doping free, and fully complementary logic inverter based on ambipolar phosphorene FETs
An All-in-One Bioinspired Neural Network
In
spite of recent advancements in artificial neural
networks (ANNs),
the energy efficiency, multifunctionality, adaptability, and integrated
nature of biological neural networks remain largely unimitated by
hardware neuromorphic computing systems. Here, we exploit optoelectronic,
computing, and programmable memory devices based on emerging two-dimensional
(2D) layered materials such as MoS2 to demonstrate a monolithically
integrated, multipixel, and āall-in-oneā bioinspired
neural network (BNN) capable of sensing, encoding, learning, forgetting,
and inferring at minuscule energy expenditure. We also demonstrate
learning adaptability and simulate learning challenges under specific
synaptic conditions to mimic biological learning. Our findings highlight
the potential of in-memory computing and sensing based on emerging
2D materials, devices, and integrated circuits to not only overcome
the bottleneck of von Neumann computing in conventional CMOS designs
but also to aid in eliminating the peripheral components necessary
for competing technologies such as memristors
An All-in-One Bioinspired Neural Network
In
spite of recent advancements in artificial neural
networks (ANNs),
the energy efficiency, multifunctionality, adaptability, and integrated
nature of biological neural networks remain largely unimitated by
hardware neuromorphic computing systems. Here, we exploit optoelectronic,
computing, and programmable memory devices based on emerging two-dimensional
(2D) layered materials such as MoS2 to demonstrate a monolithically
integrated, multipixel, and āall-in-oneā bioinspired
neural network (BNN) capable of sensing, encoding, learning, forgetting,
and inferring at minuscule energy expenditure. We also demonstrate
learning adaptability and simulate learning challenges under specific
synaptic conditions to mimic biological learning. Our findings highlight
the potential of in-memory computing and sensing based on emerging
2D materials, devices, and integrated circuits to not only overcome
the bottleneck of von Neumann computing in conventional CMOS designs
but also to aid in eliminating the peripheral components necessary
for competing technologies such as memristors
An All-in-One Bioinspired Neural Network
In
spite of recent advancements in artificial neural
networks (ANNs),
the energy efficiency, multifunctionality, adaptability, and integrated
nature of biological neural networks remain largely unimitated by
hardware neuromorphic computing systems. Here, we exploit optoelectronic,
computing, and programmable memory devices based on emerging two-dimensional
(2D) layered materials such as MoS2 to demonstrate a monolithically
integrated, multipixel, and āall-in-oneā bioinspired
neural network (BNN) capable of sensing, encoding, learning, forgetting,
and inferring at minuscule energy expenditure. We also demonstrate
learning adaptability and simulate learning challenges under specific
synaptic conditions to mimic biological learning. Our findings highlight
the potential of in-memory computing and sensing based on emerging
2D materials, devices, and integrated circuits to not only overcome
the bottleneck of von Neumann computing in conventional CMOS designs
but also to aid in eliminating the peripheral components necessary
for competing technologies such as memristors
High Performance Multilayer MoS<sub>2</sub> Transistors with Scandium Contacts
While there has been growing interest in two-dimensional
(2-D)
crystals other than graphene, evaluating their potential usefulness
for electronic applications is still in its infancy due to the lack
of a complete picture of their performance potential. The focus of
this article is on contacts. We demonstrate that through a proper
understanding and design of source/drain contacts and the right choice
of number of MoS<sub>2</sub> layers the excellent intrinsic properties
of this 2-D material can be harvested. Using scandium contacts on
10-nm-thick exfoliated MoS<sub>2</sub> flakes that are covered by
a 15 nm Al<sub>2</sub>O<sub>3</sub> film, high effective mobilities
of 700 cm<sup>2</sup>/(V s) are achieved at room temperature. This
breakthrough is largely attributed to the fact that we succeeded in
eliminating contact resistance effects that limited the device performance
in the past unrecognized. In fact, the apparent linear dependence
of current on drain voltage had mislead researchers to believe that
a truly Ohmic contact had already been achieved, a misconception that
we also elucidate in the present article
Artificial neural network based modelling approach for municpal solid waste gasification in a fluidized bed reactor
In this paper, multi-layer feed forward neural networks are used to predict the lower heating value of gas (LHV), lower heating value of gasification products including tars and entrained char (LHVp) and syngas yield during gasification of municipal solid waste (MSW) during gasification in a fluidised bed reactor. These artificial neural networks (ANNs) with different architectures are trained using the LevenbergāMarquardt (LM) back-propagation algorithm and a cross validation is also performed to ensure that the results generalise to other unseen datasets. A rigorous study is carried out on optimally choosing the number of hidden layers, number of neurons in the hidden layer and activation function in a network using multiple Monte Carlo runs. Nine input and three output parameters are used to train and test various neural network architectures in both multiple output and single output prediction paradigms using the available experimental datasets. The model selection procedure is carried out to ascertain the best network architecture in terms of predictive accuracy. The simulation results show that the ANN based methodology is a viable alternative which can be used to predict the performance of a fluidised bed gasifier