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Integration of low-power consumer electronics on glass can revolutionize the automotive and transport sectors, packaging industry, smart building and interior design, healthcare, life science engineering, display technologies, and many other applications. However, direct growth of high-performance, scalable, and reliable electronic materials on glass is difficult owing to low thermal budget. Similarly, development of energy-efficient electronic and optoelectronic devices on glass requires manufacturing innovations. Here, we accomplish both by relatively low-temperature (

Memristive crossbar architectures are evolving as powerful in-memory computing engines for artificial neural networks. However, the limited number of non-volatile conductance states offered by state-of-the-art memristors is a concern for their hardware implementation since trained weights must be rounded to the nearest conductance states, introducing error which can significantly limit inference accuracy. Moreover, the incapability of precise weight updates can lead to convergence problems and slowdown of on-chip training. In this article, we circumvent these challenges by introducing graphene-based multi-level (>16) and non-volatile memristive synapses with arbitrarily programmable conductance states. We also show desirable retention and programming endurance. Finally, we demonstrate that graphene memristors enable weight assignment based on k-means clustering, which offers greater computing accuracy when compared with uniform weight quantization for vector matrix multiplication, an essential component for any artificial neural network., Designing efficient and low power memristors-based neuromorphic systems remains a challenge. Here, the authors present graphene-based multi-level (>16) and non-volatile memristive synapses with arbitrarily programmable conductance states capable of weight assignment based on k-means clustering.

The rapid proliferation of security compromised hardware in today's integrated circuit (IC) supply chain poses a global threat to the reliability of communication, computing, and control systems. While there have been significant advancements in detection and avoidance of security breaches, current top-down approaches are mostly inadequate, inefficient, often inconclusive, and resource extensive in time, energy, and cost, offering tremendous scope for innovation in this field. Here, we introduce an energy and area efficient non-von Neumann hardware platform providing comprehensive and bottom-up security solutions by exploiting inherent device-to-device variation, electrical programmability, and persistent photoconductivity demonstrated by atomically thin two-dimensional memtransistors. We realize diverse security primitives including physically unclonable function, anticounterfeit measures, intellectual property (IP) watermarking, and IC camouflaging to prevent false authentication, detect recycled and remarked ICs, protect IP theft, and stop reverse engineering of ICs.

Integration of low-power consumer electronics on glass can revolutionize the automotive and transport sectors, packaging industry, smart building and interior design, healthcare, life science engineering, display technologies, and many other applications. However, direct growth of high-performance, scalable, and reliable electronic materials on glass is difficult owing to low thermal budget. Similarly, development of energy-efficient electronic and optoelectronic devices on glass requires manufacturing innovations. Here, we accomplish both by relatively low-temperature (600 °C) metal-organic chemical vapor deposition growth of atomically thin MoS

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. Here, we demonstrate a biomimetic device based on a dual gated MoS2 field effect transistor (FET) capable of encoding analog signals into stochastic spike trains following various neural encoding algorithms such as rate-based encoding, spike timing-based encoding, and spike count-based encoding. Two important aspects of neural encoding, namely, dynamic range and encoding precision are also captured in our demonstration. Furthermore, the encoding energy was found to be as frugal as ≈1–5 pJ/spike. Finally, we show fast (≈200 timesteps) encoding of the MNIST data set using our biomimetic device followed by more than 91% accurate inference using a trained SNN., The implementation of spiking neural network in future neuromorphic hardware requires hardware encoder analogous to the sensory neurons. The authors show a biomimetic dual-gated MoS2 field effect transistor capable of encoding analog signals into stochastic spike trains at energy cost of 1–5 pJ/spike.

In this article, we adopt a radical approach for next generation ultra-low-power sensor design by embracing the evolutionary success of animals with extraordinary sensory information processing capabilities that allow them to survive in extreme and resource constrained environments. Stochastic resonance (SR) is one of those astounding phenomena, where noise, which is considered detrimental for electronic circuits and communication systems, plays a constructive role in the detection of weak signals. Here, we show SR in a photodetector based on monolayer MoS2 for detecting ultra-low-intensity subthreshold optical signals from a distant light emitting diode (LED). We demonstrate that weak periodic LED signals, which are otherwise undetectable, can be detected by a MoS2 photodetector in the presence of a finite and optimum amount of white Gaussian noise at a frugal energy expenditure of few tens of nano-Joules. The concept of SR is generic in nature and can be extended beyond photodetector to any other sensors. Here, the authors take advantage of stochastic resonance in a photodetector based on monolayer MoS2 for measuring otherwise undetectable, ultra-low-intensity, subthreshold optical signals from a distant light emitting diode in the presence of a finite and optimum amount of white Gaussian noise.

Unplanned use of fertilizers leads to inferior quality of crops. Excess of one nutrient can make it difficult for the plant to absorb the other nutrients. To deal with this problem, the quality of soil is tested using a PH sensor that indicates the percentage of macronutrients present in the soil. Conventional methods used to test soil quality, involve the use of Ion Selective Field Effect Transistors (ISFET), Ion Selective Electrode (ISE) and Optical Sensors as the sensing units which were found to be very expensive. The prototype design will allow sprinkling of fertilizers to take place in zones which are deficient in these macronutrients (Nitrogen, Phosphorous and Potassium), proving it to be a cost efficient and farmer-friendly automated fertilization unit. Cost of the proposed unit is found to be one-seventh of that of the present methods, making it affordable for farmers and also saves the manual labor. Initial analysis and intensive case studies conducted in farmland situated near Ambedkar Nagar, Sarjapur also revealed the use of above mechanism to be more prominent and verified through practical implementation and experimentation as it takes lesser time to analyze the nutrient content than the other methods which require soil testing. Sprinklers cover discrete zones in the field that automate fertilization and reduce the effort of farmers in the rural areas. This novel technique also has a fast response time as it enables real time, in-situ soil nutrient analysis, thereby maintaining proper soil pH level required for a particular crop, reducing potentially negative environmental impacts.