Journal
ADVANCED MATERIALS
Volume 32, Issue 36, Pages -Publisher
WILEY-V C H VERLAG GMBH
DOI: 10.1002/adma.202002431
Keywords
2D materials; crossbar arrays; integrated circuits; neural networks; transition metal dichalcogenides
Categories
Funding
- Samsung Advanced Institute of Technology (SAIT) on the collaborative research for the two-dimensional-materials and their applications [A30216]
- National Science Foundation (NSF) on the Science and Technology Center for Integrated Quantum Materials [DMR-1231319]
- National Science Foundation (NSF) on the research project for quantum optoelectronics, magnetoelectronics and plasmonics in 2-dimensional materials heterostructures [EFMA-1542807]
- NSF [1541959]
Ask authors/readers for more resources
2D semiconductors, especially transition metal dichalcogenide (TMD) monolayers, are extensively studied for electronic and optoelectronic applications. Beyond intensive studies on single transistors and photodetectors, the recent advent of large-area synthesis of these atomically thin layers has paved the way for 2D integrated circuits, such as digital logic circuits and image sensors, achieving an integration level of approximate to 100 devices thus far. Here, a decisive advance in 2D integrated circuits is reported, where the device integration scale is increased by tenfold and the functional complexity of 2D electronics is propelled to an unprecedented level. Concretely, an analog optoelectronic processor inspired by biological vision is developed, where 32 x 32 = 1024 MoS(2)photosensitive field-effect transistors manifesting persistent photoconductivity (PPC) effects are arranged in a crossbar array. This optoelectronic processor with PPC memory mimics two core functions of human vision: it captures and stores an optical image into electrical data, like the eye and optic nerve chain, and then recognizes this electrical form of the captured image, like the brain, by executing analog in-memory neural net computing. In the highlight demonstration, the MoS2FET crossbar array optically images 1000 handwritten digits and electrically recognizes these imaged data with 94% accuracy.
Authors
I am an author on this paper
Click your name to claim this paper and add it to your profile.
Reviews
Recommended
No Data Available