Bio-inspired artificial synaptic transistors: evolution from innovative basic units to system integration

Abstract

The investigation of transistor-based artificial synapses in bioinspired information processing is undergoing a booming exploration, which is the stable building block for brain-like computing. Given that the storage and computing...

High-performance asymmetric electrode structured light-stimulated synaptic transistor for artificial neural networks

“…In addition, we fit the dependence of the PPF index on Dt with a double exponential function. 4,21,76 PPF index = 1 + C 1 exp(ÀDt/t 1 ) + C 2 exp(ÀDt/t 2 ), where C 1 and C 2 represent the fast-phase and slow-phase facilitation amplitudes, respectively, while Dt represents the interval time. In our example, t 1 = 173 ms and t 2 = 3000 ms.…”

“…This time scale is consistent with the range of time scales of biological synapses. 4,21,76 The brain consolidates memory through learning, and memory levels can be categorized by retention time as shortterm memory (STM) and long-term memory (LTM). In psychology, STM and LTM are regarded as the foundations of learning and memory, with the primary distinction being the longevity of memory.…”

“…Moreover, this classic architecture has a bottleneck in the data flow, which makes it take longer and use more energy to process complex data. [1][2][3][4][5][6][7] Consequently, the development of intelligent electronic devices with a quicker response time, lower energy usage, and simpler structure has received increasing attention. [8][9][10][11][12] In the past few years, there have been many attempts to construct intelligent electrical devices that work in a similar way to the nervous systems of living things interacting with their surroundings.…”

Porous crystalline materials for memories and neuromorphic computing systems

“…, artificial synapses and artificial neurons) is crucial for constructing neuromorphic computing systems capable of overcoming the von Neumann bottleneck in this post-Moore's law era. 71,72,74,75,89,393,394,479,480 Up to now, various devices, including memristor, 70,72,73,481–488 flash memory, 285,489–492 EG-FET, 293,295,296,489,490,493–496 and memtransistor, 497–499 based on different functional materials, 484,500,501 such as 2D materials, 85–88,387,502–508 perovskite, 76–80,389,509,510 biomaterials, 81,82 TMO, 385,511–513 and organic materials, 71,90,514,515 have been utilized for neuromorphic devices.…”

“…Up to now, various devices, including memristor, 70,72,73,481-488 flash memory, 285,[489][490][491][492] EG-FET, 293,295,296,489,490,493-496 and memtransistor, [497][498][499] based on different functional materials, 484,500,501 such as 2D materials, 85-88,387,502-508 perovskite, 76-80,389,509,510 biomaterials, 81,82 TMO, 385,[511][512][513] and organic materials, 71,90,514,515 have been utilized for neuromorphic devices.…”