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.