Ultrafast machine vision with 2D material neural network image sensors.

Machine vision technology has taken huge leaps in recent years, and is now becoming an integral part of various intelligent systems, including autonomous vehicles and robotics. Usually, visual information is captured by a frame-based camera, converted into a digital format and processed afterwards using a machine-learning algorithm such as an artificial neural network (ANN)1. The large amount of (mostly redundant) data passed through the entire signal chain, however, results in low frame rates and high power consumption. Various visual data preprocessing techniques have thus been developed2-7 to increase the efficiency of the subsequent signal processing in an ANN. Here we demonstrate that an image sensor can itself constitute an ANN that can simultaneously sense and process optical images without latency. Our device is based on a reconfigurable two-dimensional (2D) semiconductor8,9 photodiode10-12 array, and the synaptic weights of the network are stored in a continuously tunable photoresponsivity matrix. We demonstrate both supervised and unsupervised learning and train the sensor to classify and encode images that are optically projected onto the chip with a throughput of 20 million bins per second.

Mechanisms of photoconductivity in atomically thin MoS2.

Atomically thin transition metal dichalcogenides have emerged as promising candidates for sensitive photodetection. Here, we report a photoconductivity study of biased mono- and bilayer molybdenum disulfide field-effect transistors. We identify photovoltaic and photoconductive effects, which both show strong photogain. The photovoltaic effect is described as a shift in transistor threshold voltage due to charge transfer from the channel to nearby molecules, including SiO2 surface-bound water. The photoconductive effect is attributed to the trapping of carriers in band tail states in the molybdenum disulfide itself. A simple model is presented that reproduces our experimental observations, such as the dependence on incident optical power and gate voltage. Our findings offer design and engineering strategies for atomically thin molybdenum disulfide photodetectors, and we anticipate that the results are generalizable to other transition metal dichalcogenides as well.

In-sensor computing using a MoS2 photodetector with programmable spectral responsivity.

Optical spectroscopy is an indispensable technique in almost all areas of scientific research and industrial applications. After its acquisition, an optical spectrum is usually further processed using a mathematical algorithm to classify or quantify the measurement results. Here we present the design and realization of a smart photodetector that provides such information directly without the need to explicitly record a spectrum. This is achieved by tailoring the spectral responsivity of the device to a specific purpose. In-sensor computation is performed at the lowest possible level of the sensor system hierarchy - the physical level of photon detection - and does not require any external processing of the measurement data. The device can be programmed to cover different types of spectral regression or classification tasks. We present the analysis of spectral mixtures as an example, but the scheme can also be applied to any other algorithm that can be represented by a linear operator. Our prototype physical implementation utilizes an ensemble of optical cavity-enhanced MoS2 photodetectors with different center wavelengths and individually adjustable peak responsivities. This spectroscopy method represents a significant advance in miniaturized and energy-efficient optical sensing.

Water-based 2-dimensional anatase TiO2 inks for printed diodes and transistors.

2-Dimensional (2D) materials are attracting strong interest in printed electronics because of their unique properties and easy processability, enabling the fabrication of devices with low cost and mass scalable methods such as inkjet printing. For the fabrication of fully printed devices, it is of fundamental importance to develop a printable dielectric ink, providing good insulation and the ability to withstand large electric fields. Hexagonal boron nitride (h-BN) is typically used as a dielectric in printed devices. However, the h-BN film thickness is usually above 1 µm, hence limiting the use of h-BN in low-voltage applications. Furthermore, the h-BN ink is composed of nanosheets with broad lateral size and thickness distributions, due to the use of liquid-phase exfoliation (LPE). In this work, we investigate anatase TiO2 nanosheets (TiO2-NS), produced by a mass scalable bottom-up approach. We formulate the TiO2-NS into a water-based and printable solvent and demonstrate the use of the material with sub-micron thickness in printed diodes and transistors, hence validating the strong potential of TiO2-NS as a dielectric for printed electronics.

Sparse pixel image sensor.

As conventional frame-based cameras suffer from high energy consumption and latency, several new types of image sensors have been devised, with some of them exploiting the sparsity of natural images in some transform domain. Instead of sampling the full image, those devices capture only the coefficients of the most relevant spatial frequencies. The number of samples can be even sparser if a signal only needs to be classified rather than being fully reconstructed. Based on the corresponding mathematical framework, we developed an image sensor that can be trained to classify optically projected images by reading out the few most relevant pixels. The device is based on a two-dimensional array of metal-semiconductor-metal photodetectors with individually tunable photoresponsivity values. We demonstrate its use for the classification of handwritten digits with an accuracy comparable to that achieved by readout of the full image, but with lower delay and energy consumption.

A photosensor employing data-driven binning for ultrafast image recognition.

Pixel binning is a technique, widely used in optical image acquisition and spectroscopy, in which adjacent detector elements of an image sensor are combined into larger pixels. This reduces the amount of data to be processed as well as the impact of noise, but comes at the cost of a loss of information. Here, we push the concept of binning to its limit by combining a large fraction of the sensor elements into a single "superpixel" that extends over the whole face of the chip. For a given pattern recognition task, its optimal shape is determined from training data using a machine learning algorithm. We demonstrate the classification of optically projected images from the MNIST dataset on a nanosecond timescale, with enhanced dynamic range and without loss of classification accuracy. Our concept is not limited to imaging alone but can also be applied in optical spectroscopy or other sensing applications.

Low-voltage 2D materials-based printed field-effect transistors for integrated digital and analog electronics on paper.

Paper is the ideal substrate for the development of flexible and environmentally sustainable ubiquitous electronic systems, which, combined with two-dimensional materials, could be exploited in many Internet-of-Things applications, ranging from wearable electronics to smart packaging. Here we report high-performance MoS2 field-effect transistors on paper fabricated with a "channel array" approach, combining the advantages of two large-area techniques: chemical vapor deposition and inkjet-printing. The first allows the pre-deposition of a pattern of MoS2; the second, the printing of dielectric layers, contacts, and connections to complete transistors and circuits fabrication. Average ION/IOFF of 8 × 103 (up to 5 × 104) and mobility of 5.5 cm2 V-1 s-1 (up to 26 cm2 V-1 s-1) are obtained. Fully functional integrated circuits of digital and analog building blocks, such as logic gates and current mirrors, are demonstrated, highlighting the potential of this approach for ubiquitous electronics on paper.

Optical imaging of strain in two-dimensional crystals.

Strain engineering is widely used in material science to tune the (opto-)electronic properties of materials and enhance the performance of devices. Two-dimensional atomic crystals are a versatile playground to study the influence of strain, as they can sustain very large deformations without breaking. Various optical techniques have been employed to probe strain in two-dimensional materials, including micro-Raman and photoluminescence spectroscopy. Here we demonstrate that optical second harmonic generation constitutes an even more powerful technique, as it allows extraction of the full strain tensor with a spatial resolution below the optical diffraction limit. Our method is based on the strain-induced modification of the nonlinear susceptibility tensor due to a photoelastic effect. Using a two-point bending technique, we determine the photoelastic tensor elements of molybdenum disulfide. Once identified, these parameters allow us to spatially image the two-dimensional strain field in an inhomogeneously strained sample.

A microprocessor based on a two-dimensional semiconductor.

The advent of microcomputers in the 1970s has dramatically changed our society. Since then, microprocessors have been made almost exclusively from silicon, but the ever-increasing demand for higher integration density and speed, lower power consumption and better integrability with everyday goods has prompted the search for alternatives. Germanium and III-V compound semiconductors are being considered promising candidates for future high-performance processor generations and chips based on thin-film plastic technology or carbon nanotubes could allow for embedding electronic intelligence into arbitrary objects for the Internet-of-Things. Here, we present a 1-bit implementation of a microprocessor using a two-dimensional semiconductor-molybdenum disulfide. The device can execute user-defined programs stored in an external memory, perform logical operations and communicate with its periphery. Our 1-bit design is readily scalable to multi-bit data. The device consists of 115 transistors and constitutes the most complex circuitry so far made from a two-dimensional material.

Growth, structure and stability of sputter-deposited MoS2 thin films.

Molybdenum disulphide (MoS2) thin films have received increasing interest as device-active layers in low-dimensional electronics and also as novel catalysts in electrochemical processes such as the hydrogen evolution reaction (HER) in electrochemical water splitting. For both types of applications, industrially scalable fabrication methods with good control over the MoS2 film properties are crucial. Here, we investigate scalable physical vapour deposition (PVD) of MoS2 films by magnetron sputtering. MoS2 films with thicknesses from ≈10 to ≈1000 nm were deposited on SiO2/Si and reticulated vitreous carbon (RVC) substrates. Samples deposited at room temperature (RT) and at 400 °C were compared. The deposited MoS2 was characterized by macro- and microscopic X-ray, electron beam and light scattering, scanning and spectroscopic methods as well as electrical device characterization. We find that room-temperature-deposited MoS2 films are amorphous, of smooth surface morphology and easily degraded upon moderate laser-induced annealing in ambient conditions. In contrast, films deposited at 400 °C are nano-crystalline, show a nano-grained surface morphology and are comparatively stable against laser-induced degradation. Interestingly, results from electrical transport measurements indicate an unexpected metallic-like conduction character of the studied PVD MoS2 films, independent of deposition temperature. Possible reasons for these unusual electrical properties of our PVD MoS2 thin films are discussed. A potential application for such conductive nanostructured MoS2 films could be as catalytically active electrodes in (photo-)electrocatalysis and initial electrochemical measurements suggest directions for future work on our PVD MoS2 films.

Novel highly conductive and transparent graphene-based conductors.

Transparent conductors based on few-layer graphene (FLG) intercalated with ferric chloride (FeCl(3)) have an outstandingly low sheet resistance and high optical transparency. FeCl(3)-FLGs outperform the current limit of transparent conductors such as indium tin oxide, carbon-nanotube films, and doped graphene materials. This makes FeCl(3)-FLG materials the best transparent conductor for optoelectronic devices.