Phase-change memory (PCM) has been considered a promising candidate for solving von Neumann bottlenecks owing to its low latency, non-volatile memory property and high integration density. However, PCMs usually require a large current for the reset process by melting the phase-change material into an amorphous phase, which deteriorates the energy efficiency. Various studies have been conducted to reduce the operation current by minimizing the device dimensions, but this increases the fabrication cost while the reduction of the reset current is limited.
View Article and Find Full Text PDFMemristors are two-terminal memory devices that can change the conductance state and store analog values. Thanks to their simple structure, suitability for high-density integration, and non-volatile characteristics, memristors have been intensively studied as synapses in artificial neural network systems. Memristive synapses in neural networks have theoretically better energy efficiency compared with conventional von Neumann computing processors.
View Article and Find Full Text PDFNeuromorphic computing, an alternative for von Neumann architecture, requires synapse devices where the data can be stored and computed in the same place. The three-terminal synapse device is attractive for neuromorphic computing due to its high stability and controllability. However, high nonlinearity on weight update, low dynamic range, and incompatibility with conventional CMOS systems have been reported as obstacles for large-scale crossbar arrays.
View Article and Find Full Text PDFNeuromorphic computing, a computing paradigm inspired by the human brain, enables energy-efficient and fast artificial neural networks. To process information, neuromorphic computing directly mimics the operation of biological neurons in a human brain. To effectively imitate biological neurons with electrical devices, memristor-based artificial neurons attract attention because of their simple structure, energy efficiency, and excellent scalability.
View Article and Find Full Text PDFConductive-bridging random access memory (CBRAM) has garnered attention as a building block of non-von Neumann architectures because of scalability and parallel processing on the crossbar array. To integrate CBRAM into the back-end-of-line (BEOL) process, amorphous switching materials have been investigated for practical usage. However, both the inherent randomness of filaments and disorders of amorphous material lead to poor reliability.
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