Literature DB >> 35843869

Heterogeneous Integration of Atomically Thin Semiconductors for Non-von Neumann CMOS.

Rahul Pendurthi1, Darsith Jayachandran1, Azimkhan Kozhakhmetov2, Nicholas Trainor2,3, Joshua A Robinson2,3, Joan M Redwing2,3, Saptarshi Das1,2,3,4.   

Abstract

Atomically thin, 2D, and semiconducting transition metal dichalcogenides (TMDs) are seen as potential candidates for complementary metal oxide semiconductor (CMOS) technology in future nodes. While high-performance field effect transistors (FETs), logic gates, and integrated circuits (ICs) made from n-type TMDs such as MoS2 and WS2 grown at wafer scale have been demonstrated, realizing CMOS electronics necessitates integration of large area p-type semiconductors. Furthermore, the physical separation of memory and logic is a bottleneck of the existing CMOS technology and must be overcome to reduce the energy burden for computation. In this article, the existing limitations are overcome and for the first time, a heterogeneous integration of large area grown n-type MoS2 and p-type vanadium doped WSe2 FETs with non-volatile and analog memory storage capabilities to achieve a non-von Neumann 2D CMOS platform is introduced. This manufacturing process flow allows for precise positioning of n-type and p-type FETs, which is critical for any IC development. Inverters and a simplified 2-input-1-output multiplexers and neuromorphic computing primitives such as Gaussian, sigmoid, and tanh activation functions using this non-von Neumann 2D CMOS platform are also demonstrated. This demonstration shows the feasibility of heterogeneous integration of wafer scale 2D materials.
© 2022 Wiley-VCH GmbH.

Entities:  

Keywords:  complementary logic; field-effect transistors; heterogeneous integration; integrated circuits; two-dimensional materials

Year:  2022        PMID: 35843869     DOI: 10.1002/smll.202202590

Source DB:  PubMed          Journal:  Small        ISSN: 1613-6810            Impact factor:   15.153


  1 in total

1.  Two-dimensional materials-based probabilistic synapses and reconfigurable neurons for measuring inference uncertainty using Bayesian neural networks.

Authors:  Amritanand Sebastian; Rahul Pendurthi; Azimkhan Kozhakhmetov; Nicholas Trainor; Joshua A Robinson; Joan M Redwing; Saptarshi Das
Journal:  Nat Commun       Date:  2022-10-17       Impact factor: 17.694

  1 in total

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