Wafer-scale functional circuits based on two dimensional semiconductors with fabrication optimized by machine learning

AbstractTriggered by the pioneering research on graphene, the family of two-dimensional layered materials (2DLMs) has been investigated for more than a decade, and appealing functionalities have been demonstrated. However, there are still challenges inhibiting high-quality growth and circuit-level integration, and results from previous studies are still far from complying with industrial standards. Here, we overcome these challenges by utilizing machine-learning (ML) algorithms to evaluate key process parameters that impact the electrical characteristics of MoS2 top-gated field-effect transistors (FETs). The wafer-scale fabrication processes are then guided by ML combined with grid searching to co-optimize device performance, including mobility, threshold voltage and subthreshold swing. A 62-level SPICE modeling was implemented for MoS2 FETs and further used to construct functional digital, analog, and photodetection circuits. Finally, we present wafer-scale test FET arrays and a 4-bit full adder employing industry-standard design flows and processes. Taken together, these results experimentally validate the application potential of ML-assisted fabrication optimization for beyond-silicon electronic materials.

Download Full-text

Wafer-Scale Functional Circuits Based on Two Dimensional Semiconductors with Fabrication Optimized by Machine Learning

10.21203/rs.3.rs-152918/v1 ◽

2021 ◽

Author(s):

Xinyu Chen ◽

Yufeng Xie ◽

Yaochen Sheng ◽

Hongwei Tang ◽

Zeming Wang ◽

...

Keyword(s):

Machine Learning ◽

Electronic Materials ◽

Field Effect Transistors ◽

Full Adder ◽

Two Dimensional ◽

Wafer Scale ◽

Industry Standard ◽

Standard Design ◽

Application Potential ◽

Industrial Standards

Abstract Triggered by the pioneering research on graphene, the family of two-dimensional layered materials (2DLMs) has been investigated for more than a decade, and appealing functionalities have been demonstrated. However, there are still challenges inhibiting high-quality growth and circuit-level integration, and results from previous studies are still far from complying with industrial standards. Here, we overcome these challenges by utilizing machine-learning (ML) algorithms to evaluate key process parameters that impact the electrical characteristics of MoS2 top-gated field-effect transistors (FETs). The wafer-scale fabrication processes are then guided by ML combined with grid searching to co-optimize device performance, including mobility, threshold voltage and subthreshold swing. A 62-level SPICE modeling was implemented for MoS2 FETs and further used to construct functional digital, analog, and photodetection circuits. Finally, we present wafer-scale test FET arrays and a 4-bit full adder employing industry-standard design flows and processes. Taken together, these results experimentally validate the application potential of ML-assisted fabrication optimization for beyond-silicon electronic materials.

Download Full-text

Top gate engineering of field-effect transistors based on wafer-scale two-dimensional semiconductors

Journal of Material Science and Technology ◽

10.1016/j.jmst.2021.08.021 ◽

2021 ◽

Author(s):

Jingyi Ma ◽

Xinyu Chen ◽

Yaochen Sheng ◽

Ling Tong ◽

Xiaojiao Guo ◽

...

Keyword(s):

Field Effect ◽

Field Effect Transistors ◽

Two Dimensional ◽

Wafer Scale ◽

Gate Engineering

Download Full-text

An Artificial Neutral Network Chip Based on Two-Dimensional Semiconductor

10.21203/rs.3.rs-101486/v1 ◽

2020 ◽

Author(s):

Shunli Ma ◽

Tianxiang Wu ◽

Xinyu Chen ◽

Yin Wang ◽

Hongwei Tang ◽

...

Keyword(s):

Integrated Circuits ◽

Integrated Circuit ◽

Large Scale ◽

Field Effect Transistors ◽

Recognition Rate ◽

Activation Function ◽

Machine Learning Algorithms ◽

Two Dimensional ◽

Wafer Scale ◽

Large Current

Abstract Two-dimensional semiconductors can be used to build integrated circuits for running artificial neural networks (ANN) with higher energy efficiency. The implementation of an ANN with 2D semiconductors has been held back by the large-scale and high-quality transistors required for running machine learning algorithms. Here we demonstrate the first functional MoS2 analog ANN integrated circuit, including memory, multiply-and-accumulate (MAC), activation function, and weight update circuits. The ANN integrated circuit is realized through 818 field effect transistors (FETs) with wafer-scale and high-homogeneity MoS2 film. The large current on/off ratio and output linearity of these MoS2 FETs allow the realization of convolutional and activation function circuits with a few number of transistors. This ANN can be used for recognizing tactile digit, showing the recognition rate exceeding 97%. Our work demonstrates wafer-scale processing of a 2D semiconductor for building integrated circuits with the functions of AI computation.

Download Full-text

Controlled crack propagation for atomic precision handling of wafer-scale two-dimensional materials

Science ◽

10.1126/science.aat8126 ◽

2018 ◽

Vol 362 (6415) ◽

pp. 665-670 ◽

Cited By ~ 69

Author(s):

Jaewoo Shim ◽

Sang-Hoon Bae ◽

Wei Kong ◽

Doyoon Lee ◽

Kuan Qiao ◽

...

Keyword(s):

Field Effect Transistors ◽

Hexagonal Boron Nitride ◽

2D Materials ◽

Electronic Conductivity ◽

Single Atom ◽

Adhesive Tape ◽

Two Dimensional ◽

Wafer Scale ◽

Splitting Technique ◽

Direct Growth

Although flakes of two-dimensional (2D) heterostructures at the micrometer scale can be formed with adhesive-tape exfoliation methods, isolation of 2D flakes into monolayers is extremely time consuming because it is a trial-and-error process. Controlling the number of 2D layers through direct growth also presents difficulty because of the high nucleation barrier on 2D materials. We demonstrate a layer-resolved 2D material splitting technique that permits high-throughput production of multiple monolayers of wafer-scale (5-centimeter diameter) 2D materials by splitting single stacks of thick 2D materials grown on a single wafer. Wafer-scale uniformity of hexagonal boron nitride, tungsten disulfide, tungsten diselenide, molybdenum disulfide, and molybdenum diselenide monolayers was verified by photoluminescence response and by substantial retention of electronic conductivity. We fabricated wafer-scale van der Waals heterostructures, including field-effect transistors, with single-atom thickness resolution.

Download Full-text

Construction of two-dimensional regression models of current-voltage characteristics of the output current of field-effect transistors using the information system PLExp

Izmeritel`naya Tekhnika ◽

10.32446/0368-1025it.2019-11-49-55 ◽

2019 ◽

pp. 49-55

Author(s):

N.Y. Baturina ◽

I.V. Kalienko

Keyword(s):

Information System ◽

Regression Models ◽

Field Effect ◽

Field Effect Transistors ◽

Two Dimensional ◽

Output Current ◽

Current Voltage ◽

Current Voltage Characteristics

Download Full-text

Machine Learning Based Predictive Action on Categorical Non-Sequential Data

Recent Advances in Computer Science and Communications ◽

10.2174/2213275912666190417150421 ◽

2020 ◽

Vol 13 (5) ◽

pp. 1020-1030

Author(s):

Pradeep S. ◽

Jagadish S. Kallimani

Keyword(s):

Machine Learning ◽

Data Analysis ◽

Categorical Data ◽

Numerical Data ◽

Processing Technique ◽

Machine Learning Algorithms ◽

Sequential Data ◽

Industry Standard ◽

Robust Model ◽

Future Work

Background: With the advent of data analysis and machine learning, there is a growing impetus of analyzing and generating models on historic data. The data comes in numerous forms and shapes with an abundance of challenges. The most sorted form of data for analysis is the numerical data. With the plethora of algorithms and tools it is quite manageable to deal with such data. Another form of data is of categorical nature, which is subdivided into, ordinal (order wise) and nominal (number wise). This data can be broadly classified as Sequential and Non-Sequential. Sequential data analysis is easier to preprocess using algorithms. Objective: The challenge of applying machine learning algorithms on categorical data of nonsequential nature is dealt in this paper. Methods: Upon implementing several data analysis algorithms on such data, we end up getting a biased result, which makes it impossible to generate a reliable predictive model. In this paper, we will address this problem by walking through a handful of techniques which during our research helped us in dealing with a large categorical data of non-sequential nature. In subsequent sections, we will discuss the possible implementable solutions and shortfalls of these techniques. Results: The methods are applied to sample datasets available in public domain and the results with respect to accuracy of classification are satisfactory. Conclusion: The best pre-processing technique we observed in our research is one hot encoding, which facilitates breaking down the categorical features into binary and feeding it into an Algorithm to predict the outcome. The example that we took is not abstract but it is a real – time production services dataset, which had many complex variations of categorical features. Our Future work includes creating a robust model on such data and deploying it into industry standard applications.

Download Full-text

Polarization-sensitive photodetectors based on three-dimensional molybdenum disulfide (MoS2) field-effect transistors

Nanophotonics ◽

10.1515/nanoph-2020-0401 ◽

2020 ◽

Vol 9 (16) ◽

pp. 4719-4728

Author(s):

Tao Deng ◽

Shasha Li ◽

Yuning Li ◽

Yang Zhang ◽

Jingye Sun ◽

...

Keyword(s):

Molybdenum Disulfide ◽

Field Effect ◽

High Performance ◽

Field Effect Transistors ◽

Three Dimensional ◽

Polarized Light ◽

Optical Field ◽

Two Dimensional ◽

Polarization Ratio ◽

Two Dimensional Materials

AbstractThe molybdenum disulfide (MoS2)-based photodetectors are facing two challenges: the insensitivity to polarized light and the low photoresponsivity. Herein, three-dimensional (3D) field-effect transistors (FETs) based on monolayer MoS2 were fabricated by applying a self–rolled-up technique. The unique microtubular structure makes 3D MoS2 FETs become polarization sensitive. Moreover, the microtubular structure not only offers a natural resonant microcavity to enhance the optical field inside but also increases the light-MoS2 interaction area, resulting in a higher photoresponsivity. Photoresponsivities as high as 23.8 and 2.9 A/W at 395 and 660 nm, respectively, and a comparable polarization ratio of 1.64 were obtained. The fabrication technique of the 3D MoS2 FET could be transferred to other two-dimensional materials, which is very promising for high-performance polarization-sensitive optical and optoelectronic applications.

Download Full-text