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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 MoS 2 photodetectors with different center wavelengths and individually adjustable peak responsivities. This spectroscopy method represents a significant advance in miniaturized and energy-efficient optical sensing., (© 2023. The Author(s).)

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., (© 2022. The Author(s).)

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., (© 2022. The Author(s).)

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 MoS 2 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 MoS 2 ; the second, the printing of dielectric layers, contacts, and connections to complete transistors and circuits fabrication. Average I ON /I OFF of 8 × 10 3 (up to 5 × 10 4 ) and mobility of 5.5 cm 2 V -1 s -1 (up to 26 cm 2 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.

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 developed 2-7 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) semiconductor 8,9 photodiode 10-12 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.

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.

Molybdenum disulphide (MoS 2 ) 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 MoS 2 film properties are crucial. Here, we investigate scalable physical vapour deposition (PVD) of MoS 2 films by magnetron sputtering. MoS 2 films with thicknesses from ≈10 to ≈1000 nm were deposited on SiO 2 /Si and reticulated vitreous carbon (RVC) substrates. Samples deposited at room temperature (RT) and at 400 °C were compared. The deposited MoS 2 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 MoS 2 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 MoS 2 films, independent of deposition temperature. Possible reasons for these unusual electrical properties of our PVD MoS 2 thin films are discussed. A potential application for such conductive nanostructured MoS 2 films could be as catalytically active electrodes in (photo-)electrocatalysis and initial electrochemical measurements suggest directions for future work on our PVD MoS 2 films.

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.