Emerging non-volatile (or long-term memory, LTM) memories and their crossbar integration have shown advantages as hardware synapses 8, 9, 10, 11, 12, 13, 14, 15. Moreover, the von Neumann bottleneck, an inherent limitation of digital hardware, further hinders the execution efficiency of ANNs. Nowadays, ANNs predominantly operate on digital hardware, which is not improving at an exponential pace anymore due to the slowdown of Moore’s Law, limiting the ability to make increasingly complex ANNs without increasing compute times. The human brain is a biological neural network (BioNN) composed of neurons, dendrites, and synapses, which has inspired the development of artificial neural networks (ANNs) that have had transformative impacts on computer vision, speech recognition 6, and bioinformatics 7. Neuromorphic computing seeks to emulate the structure and functions of the human brain using electronic counterparts, thus replicating its area- and energy-efficiency 1, 2, 3, 4, 5. The human brain possesses unparalleled cognitive capabilities, occupies a small footprint (roughly the size of a football ~1200−1700 cm 3, depending on individuals), and yet consumes very little power (approximately 20 W). Our results suggest that the neurotransistor-based approach may pave the way for next-generation neuromorphic computing with energy efficiency on par with those of the brain. We also showcase the potential of neurotransistors in realizing advanced brain-like functions by developing a hardware neural network and demonstrating bio-inspired sound localization. Leveraging this neurotransistor, we demonstrate dendrite integration as well as digital and analog dendritic computing for coincidence detection. In this study, we report on the development of a compact and low-power neurotransistor based on a vertical dual-gate electrolyte-gated transistor (EGT) with short-term memory characteristics, a 30 nm channel length, a record-low read power of ~3.16 fW and a biology-comparable read energy of ~30 fJ. While there has been a recent surge of interest in implementing dendritic computing using emerging devices, achieving artificial dendrites with throughputs and energy efficiency comparable to those of the human brain has proven challenging. One promising approach is dendritic computing, which takes inspiration from the multi-dendritic branch structure of neurons to enhance the processing capability of artificial neural networks. This micrograph (right), taken with a reflected light microscope, shows the appearance of dendrites of a copper-tin alloy when observed as a 2D section through the 3D structure.Neuromorphic computing aims to emulate the computing processes of the brain by replicating the functions of biological neural networks using electronic counterparts. This micrograph (left) is an image of the 3D structure of dendrites in a cobalt-samarium-copper alloy, taken with a To see more about how a microstructure develops in a casting, see the microstructure of a cast ingot section of the TLP on Casting. Grains which are orientated with the 〈 100 〉 direction close to the direction of heat flow will grow fastest and stifle the growth of other grains, leading to a columnar microstructure. When a dendritic structure forms, the dendrite arms grow parallel to the favourable growth directions, normally 〈 100 〉 in cubic metals. The animation referred to the driving force for solidification, which is greater for larger undercoolings. The animation below shows how the temperature gradient in the liquid affects the morphology of the growth front in a pure metal:
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