A Graphene-Based Straintronic Physically Unclonable Function.

Physically unclonable functions (PUFs) are an integral part of modern-day hardware security. Various types of PUFs already exist, including optical, electronic, and magnetic PUFs. Here, we introduce a novel straintronic PUF (SPUF) by exploiting strain-induced reversible cracking in the contact microstructures of graphene field-effect transistors (GFETs).

Ultrascaled Contacts to Monolayer MoS Field Effect Transistors.

Two-dimensional (2D) semiconductors possess promise for the development of field-effect transistors (FETs) at the ultimate scaling limit due to their strong gate electrostatics. However, proper FET scaling requires reduction of both channel length () and contact length (), the latter of which has remained a challenge due to increased current crowding at the nanoscale. Here, we investigate Au contacts to monolayer MoS FETs with down to 100 nm and down to 20 nm to evaluate the impact of contact scaling on FET performance.

Active pixel sensor matrix based on monolayer MoS phototransistor array.

In-sensor processing, which can reduce the energy and hardware burden for many machine vision applications, is currently lacking in state-of-the-art active pixel sensor (APS) technology. Photosensitive and semiconducting two-dimensional (2D) materials can bridge this technology gap by integrating image capture (sense) and image processing (compute) capabilities in a single device. Here, we introduce a 2D APS technology based on a monolayer MoS phototransistor array, where each pixel uses a single programmable phototransistor, leading to a substantial reduction in footprint (900 pixels in ∼0.

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 MoS to demonstrate a monolithically integrated, multipixel, and "all-in-one" 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.

Bioinspired and Low-Power 2D Machine Vision with Adaptive Machine Learning and Forgetting.

Natural intelligence has many dimensions, with some of its most important manifestations being tied to learning about the environment and making behavioral changes. In primates, vision plays a critical role in learning. The underlying biological neural networks contain specialized neurons and synapses which not only sense and process visual stimuli but also learn and adapt with remarkable energy efficiency.

A Sparse and Spike-Timing-Based Adaptive Photoencoder for Augmenting Machine Vision for Spiking Neural Networks.

The representation of external stimuli in the form of action potentials or spikes constitutes the basis of energy efficient neural computation that emerging spiking neural networks (SNNs) aspire to imitate. With recent evidence suggesting that information in the brain is more often represented by explicit firing times of the neurons rather than mean firing rates, it is imperative to develop novel hardware that can accelerate sparse and spike-timing-based encoding. Here a medium-scale integrated circuit composed of two cascaded three-stage inverters and one XOR logic gate fabricated using a total of 21 memtransistors based on photosensitive 2D monolayer MoS  for spike-timing-based encoding of visual information, is introduced.

A biomimetic neural encoder for spiking neural network.

Spiking neural networks (SNNs) promise to bridge the gap between artificial neural networks (ANNs) and biological neural networks (BNNs) by exploiting biologically plausible neurons that offer faster inference, lower energy expenditure, and event-driven information processing capabilities. However, implementation of SNNs in future neuromorphic hardware requires hardware encoders analogous to the sensory neurons, which convert external/internal stimulus into spike trains based on specific neural algorithm along with inherent stochasticity. Unfortunately, conventional solid-state transducers are inadequate for this purpose necessitating the development of neural encoders to serve the growing need of neuromorphic computing.

Gaussian synapses for probabilistic neural networks.

The recent decline in energy, size and complexity scaling of traditional von Neumann architecture has resurrected considerable interest in brain-inspired computing. Artificial neural networks (ANNs) based on emerging devices, such as memristors, achieve brain-like computing but lack energy-efficiency. Furthermore, slow learning, incremental adaptation, and false convergence are unresolved challenges for ANNs.