PT  - JOURNAL ARTICLE
AU  - Kim, Sungi
AU  - Kim, Namjun
AU  - Seo, Jinyoung
AU  - Park, Jeong-Eun
AU  - Song, Eun Ho
AU  - Choi, So Young
AU  - Kim, Ji Eun
AU  - Cha, Seungsang
AU  - Park, Ha H.
AU  - Nam, Jwa-Min
TI  - Nanoparticle-based computing architecture for nanoparticle neural networks
AID  - 10.1126/sciadv.abb3348
DP  - 2020 Aug 01
TA  - Science Advances
PG  - eabb3348
VI  - 6
IP  - 35
4099  - http://advances.sciencemag.org/content/6/35/eabb3348.short
4100  - http://advances.sciencemag.org/content/6/35/eabb3348.full
SO  - Sci Adv2020 Aug 01; 6
AB  - The lack of a scalable nanoparticle-based computing architecture severely limits the potential and use of nanoparticles for manipulating and processing information with molecular computing schemes. Inspired by the von Neumann architecture (VNA), in which multiple programs can be operated without restructuring the computer, we realized the nanoparticle-based VNA (NVNA) on a lipid chip for multiple executions of arbitrary molecular logic operations in the single chip without refabrication. In this system, nanoparticles on a lipid chip function as the hardware that features memory, processors, and output units, and DNA strands are used as the software to provide molecular instructions for the facile programming of logic circuits. NVNA enables a group of nanoparticles to form a feed-forward neural network, a perceptron, which implements functionally complete Boolean logic operations, and provides a programmable, resettable, scalable computing architecture and circuit board to form nanoparticle neural networks and make logical decisions.