TY - JOUR
T1 - Additive Manufacturing of Neuromorphic Systems
AU - Yan, Jiongyi
AU - Su, Yutai
AU - Armstrong, James P.K.
AU - Gleadall, Andrew
N1 - Publisher Copyright:
© 2025 The Author(s). Advanced Materials published by Wiley-VCH GmbH.
PY - 2025/10/2
Y1 - 2025/10/2
N2 - Neuromorphic engineering aims to create brain-inspired computing systems based on synaptic electronic hardware and neural network software. It combines intelligent materials, advanced processing technology, and computation programs. Additive manufacturing (AM), despite being one of the advanced manufacturing technologies capable of multimaterial processing at the microscale, is not widely applied in neuromorphic hardware fabrication. This gap suggests not only process incompatibility and limited resolution of AM but opportunities to create novel intelligent systems. Here, the state-of-the-art in AM-printed neuromorphic hardware (synaptic electronics and mechanical systems) is reviewed and discussed the integration of AM techniques with neuromorphic engineering. An outlook of printed neuromorphic systems is provided with low cost and environmental impact but high customizability and design flexibility. With ongoing innovation in AM technologies and materials, AM is envisioned with high throughput and resolution for affordable, scalable, and customizable neuromorphic hardware production. The crossover of AM and neuromorphic engineering facilitates prototyping of brain-inspired computing architectures for efficient and analog computation. This approach may facilitate various applications including neuromorphic robotics, bionics, and real-time sensing.
AB - Neuromorphic engineering aims to create brain-inspired computing systems based on synaptic electronic hardware and neural network software. It combines intelligent materials, advanced processing technology, and computation programs. Additive manufacturing (AM), despite being one of the advanced manufacturing technologies capable of multimaterial processing at the microscale, is not widely applied in neuromorphic hardware fabrication. This gap suggests not only process incompatibility and limited resolution of AM but opportunities to create novel intelligent systems. Here, the state-of-the-art in AM-printed neuromorphic hardware (synaptic electronics and mechanical systems) is reviewed and discussed the integration of AM techniques with neuromorphic engineering. An outlook of printed neuromorphic systems is provided with low cost and environmental impact but high customizability and design flexibility. With ongoing innovation in AM technologies and materials, AM is envisioned with high throughput and resolution for affordable, scalable, and customizable neuromorphic hardware production. The crossover of AM and neuromorphic engineering facilitates prototyping of brain-inspired computing architectures for efficient and analog computation. This approach may facilitate various applications including neuromorphic robotics, bionics, and real-time sensing.
KW - additive manufacturing
KW - artificial neural networks
KW - memristors
KW - neuromorphic computing
KW - transistors
UR - https://www.scopus.com/pages/publications/105010336803
U2 - 10.1002/adma.202504807
DO - 10.1002/adma.202504807
M3 - 文献综述
AN - SCOPUS:105010336803
SN - 0935-9648
VL - 37
JO - Advanced Materials
JF - Advanced Materials
IS - 39
M1 - 2504807
ER -