Long-Range Epitaxial MOF Electronics for Continuous Monitoring of Human Breath Ammonia.

As an important biomarker, ammonia exhibits a strong correlation with protein metabolism and specific organ dysfunction. Limited by the immobile instrumental structure, invasive and complicated procedures, and unsatisfactory online sensitivity and selectivity, current medical diagnosis fails to monitor this chemical in real time efficiently. Herein, we present the successful synthesis of a long-range epitaxial metal-organic framework on a millimeter domain-sized single-crystalline graphene substrate (LR-epi-MOF). With a perfect 30° epitaxial angle and a mere 2.8% coincidence site lattice mismatch between the MOF and graphene, this long-range-ordered epitaxial structure boosts the charge transfer from ammonia to the MOF and then to graphene, thereby promoting the overall charge delocalization and exhibiting extraordinary electrical global coupling properties. This unique characteristic imparts a remarkable sensitivity of 0.1 ppb toward ammonia. The sub-ppb detecting capability and high anti-interference ability enable continuous information recording of breath ammonia that is strongly correlated with the intriguing human lifestyle. Wearable electronics based on the LR-epi-MOF could accurately portray the active protein metabolism pattern in real time and provide personal assistance in health management.

Hybrid neuromorphic hardware with sparing 2D synapse and CMOS neuron for character recognition.

Neuromorphic computing enables efficient processing of data-intensive tasks, but requires numerous artificial synapses and neurons for certain functions, which leads to bulky systems and energy challenges. Achieving functionality with fewer synapses and neurons will facilitate integration density and computility. Two-dimensional (2D) materials exhibit potential for artificial synapses, including diverse biomimetic plasticity and efficient computing. Considering the complexity of neuron circuits and the maturity of complementary metal-oxide-semiconductor (CMOS), hybrid integration is attractive. Here, we demonstrate a hybrid neuromorphic hardware with 2D MoS2 synaptic arrays and CMOS neural circuitry integrated on board. With the joint benefit of hybrid integration, frequency coding and feature extraction, a total cost of twelve MoS2 synapses, three CMOS neurons, combined with digital/analogue converter enables alphabetic and numeric recognition. MoS2 synapses exhibit progressively tunable weight plasticity, CMOS neurons integrate and fire frequency-encoded spikes to display the target characters. The synapse- and neuron-saving hybrid hardware exhibits a competitive accuracy of 98.8% and single recognition energy consumption of 11.4 µW. This work provides a viable solution for building neuromorphic hardware with high compactness and computility.

An artificial neural network chip based on two-dimensional semiconductor.

Recently, research on two-dimensional (2D) semiconductors has begun to translate from the fundamental investigation into rudimentary functional circuits. In this work, we unveil the first functional MoS2 artificial neural network (ANN) chip, including multiply-and-accumulate (MAC), memory and activation function circuits. Such MoS2 ANN chip is realized through fabricating 818 field-effect transistors (FETs) on a wafer-scale and high-homogeneity MoS2 film, with a gate-last process to realize top gate structured FETs. A 62-level simulation program with integrated circuit emphasis (SPICE) model is utilized to design and optimize our analog ANN circuits. To demonstrate a practical application, a tactile digit sensing recognition was demonstrated based on our ANN circuits. After training, the digit recognition rate exceeds 97%. Our work not only demonstrates the protentional of 2D semiconductors in wafer-scale integrated circuits, but also paves the way for its future application in AI computation.

A 619-pixel machine vision enhancement chip based on two-dimensional semiconductors.

The rapid development of machine vision applications demands hardware that can sense and process visual information in a single monolithic unit to avoid redundant data transfer. Here, we design and demonstrate a monolithic vision enhancement chip with light-sensing, memory, digital-to-analog conversion, and processing functions by implementing a 619-pixel with 8582 transistors and physical dimensions of 10 mm by 10 mm based on a wafer-scale two-dimensional (2D) monolayer molybdenum disulfide (MoS2). The light-sensing function with analog MoS2 transistor circuits offers low noise and high photosensitivity. Furthermore, we adopt a MoS2 analog processing circuit to dynamically adjust the photocurrent of individual imaging sensor, which yields a high dynamic light-sensing range greater than 90 decibels. The vision chip allows the applications for contrast enhancement and noise reduction of image processing. This large-scale monolithic chip based on 2D semiconductors shows multiple functions with light sensing, memory, and processing for artificial machine vision applications, exhibiting the potentials of 2D semiconductors for future electronics.

Pass-Transistor Logic Circuits Based on Wafer-Scale 2D Semiconductors.

2D semiconductors, such as molybdenum disulfide (MoS2 ), have attracted tremendous attention in constructing advanced monolithic integrated circuits (ICs) for future flexible and energy-efficient electronics. However, the development of large-scale ICs based on 2D materials is still in its early stage, mainly due to the non-uniformity of the individual devices and little investigation of device and circuit-level optimization. Herein, a 4-inch high-quality monolayer MoS2 film is successfully synthesized, which is then used to fabricate top-gated (TG) MoS2 field-effect transistors with wafer-scale uniformity. Some basic circuits such as static random access memory and ring oscillators are examined. A pass-transistor logic configuration based on pseudo-NMOS is then employed to design more complex MoS2 logic circuits, which are successfully fabricated with proper logic functions tested. These preliminary integration efforts show the promising potential of wafer-scale 2D semiconductors for application in complex ICs.

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

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.

An in-memory computing architecture based on two-dimensional semiconductors for multiply-accumulate operations.

In-memory computing may enable multiply-accumulate (MAC) operations, which are the primary calculations used in artificial intelligence (AI). Performing MAC operations with high capacity in a small area with high energy efficiency remains a challenge. In this work, we propose a circuit architecture that integrates monolayer MoS2 transistors in a two-transistor-one-capacitor (2T-1C) configuration. In this structure, the memory portion is similar to a 1T-1C Dynamic Random Access Memory (DRAM) so that theoretically the cycling endurance and erase/write speed inherit the merits of DRAM. Besides, the ultralow leakage current of the MoS2 transistor enables the storage of multi-level voltages on the capacitor with a long retention time. The electrical characteristics of a single MoS2 transistor also allow analog computation by multiplying the drain voltage by the stored voltage on the capacitor. The sum-of-product is then obtained by converging the currents from multiple 2T-1C units. Based on our experiment results, a neural network is ex-situ trained for image recognition with 90.3% accuracy. In the future, such 2T-1C units can potentially be integrated into three-dimensional (3D) circuits with dense logic and memory layers for low power in-situ training of neural networks in hardware.

Two-dimensional ferroelectric channel transistors integrating ultra-fast memory and neural computing.

With the advent of the big data era, applications are more data-centric and energy efficiency issues caused by frequent data interactions, due to the physical separation of memory and computing, will become increasingly severe. Emerging technologies have been proposed to perform analog computing with memory to address the dilemma. Ferroelectric memory has become a promising technology due to field-driven fast switching and non-destructive readout, but endurance and miniaturization are limited. Here, we demonstrate the α-In2Se3 ferroelectric semiconductor channel device that integrates non-volatile memory and neural computation functions. Remarkable performance includes ultra-fast write speed of 40 ns, improved endurance through the internal electric field, flexible adjustment of neural plasticity, ultra-low energy consumption of 234/40 fJ per event for excitation/inhibition, and thermally modulated 94.74% high-precision iris recognition classification simulation. This prototypical demonstration lays the foundation for an integrated memory computing system with high density and energy efficiency.