In-memory computing featuring a radical departure from the von Neumann architecture is promising to substantially reduce the energy and time consumption for data-intensive computation. With the increasing challenges facing silicon complementary metal-oxide-semiconductor (CMOS) technology, developing in-memory computing hardware would require a different platform to deliver significantly enhanced functionalities at the material and device level. Here, we explore a dual-gate two-dimensional ferroelectric field-effect transistor (2D FeFET) as a basic device to form both nonvolatile logic gates and artificial synapses, addressing in-memory computing simultaneously in digital and analog spaces. Through diversifying the electrostatic behaviors in 2D transistors with the dual-ferroelectric-coupling effect, rich logic functionalities including linear (AND, OR) and nonlinear (XNOR) gates were obtained in unipolar (MoS2) and ambipolar (MoTe2) FeFETs. Combining both types of 2D FeFETs in a heterogeneous platform, an important computation circuit, i.e., a half-adder, was successfully constructed with an area-efficient two-transistor structure. Furthermore, with the same device structure, several key synaptic functions are shown at the device level, and an artificial neural network is simulated at the system level, manifesting its potential for neuromorphic computing. These findings highlight the prospects of dual-gate 2D FeFETs for the development of multifunctional in-memory computing hardware capable of both digital and analog computation.

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Dual-Ferroelectric-Coupling-Engineered Two-Dimensional Transistors for Multifunctional In-Memory Computing.

Ferroelectric order in van der Waals layered materials

With the total amount of worldwide data skyrocketing, the global data storage demand is predicted to grow to 1.75 × 1014 GB by 2025. Traditional storage methods have difficulties keeping pace given that current storage media have a maximum density of 103 GB/mm3. As such, data production will far exceed the capacity of currently available storage methods. The costs of maintaining and transferring data, as well as the limited lifespans and significant data losses associated with current technologies also demand advanced solutions for information storage. Nature offers a powerful alternative through the storage of information that defines living organisms in unique orders of four bases (A, T, C, G) located in molecules called deoxyribonucleic acid (DNA). DNA molecules as information carriers have many advantages over traditional storage media. Their high storage density, potentially low maintenance cost, ease of synthesis, and chemical modification make them an ideal alternative for information storage. To this end, rapid progress has been made over the past decade by exploiting user-defined DNA materials to encode information. In this review, we discuss the most recent advances of DNA-based data storage with a major focus on the challenges that remain in this promising field, including the current intrinsic low speed in data writing and reading and the high cost per byte stored. Alternatively, data storage relying on DNA nanostructures (as opposed to DNA sequence) as well as on other combinations of nanomaterials and biomolecules are proposed with promising technological and economic advantages. In summarizing the advances that have been made and underlining the challenges that remain, we provide a roadmap for the ongoing research in this rapidly growing field, which will enable the development of technological solutions to the global demand for superior storage methodologies.

https://zenodo.org/record/7341397/files/2022_Acs_nano_review.pdf

Emerging Approaches to DNA Data Storage: Challenges and Prospects

Ferroelectric materials play an important role in a wide spectrum of semiconductor technologies and device applications. Two-dimensional (2D) van der Waals (vdW) ferroelectrics with surface-insensitive ferroelectricity that is significantly different from their traditional bulk counterparts have further inspired intensive interest. Integration of ferroelectrics into 2D-layered-material-based devices is expected to offer intriguing working principles and add desired functionalities for next-generation electronics. Herein, fundamental properties of ferroelectric materials that are compatible with 2D devices are introduced, followed by a critical review of recent advances on the integration of ferroelectrics into 2D devices. Representative device architectures and corresponding working mechanisms are discussed, such as ferroelectrics/2D semiconductor heterostructures, 2D ferroelectric tunnel junctions, and 2D ferroelectric diodes. By leveraging the favorable properties of ferroelectrics, a variety of functional 2D devices including ferroelectric-gated negative capacitance field-effect transistors, programmable devices, nonvolatile memories, and neuromorphic devices are highlighted, where the application of 2D vdW ferroelectrics is particularly emphasized. This review provides a comprehensive understanding of ferroelectrics-integrated 2D devices and discusses the challenges of applying them into commercial electronic circuits.

Ferroelectrics-Integrated Two-Dimensional Devices toward Next-Generation Electronics.

2D materials possess superior optoelectronic properties, such as ultrahigh mobility and broadband photoresponse, making them one of the most vital platforms for diversified photodetectors. However, atomic thickness 2D materials usually suffer from intrinsic low absorption. To promote the photodetector performance, a feasible method is to integrate the 2D materials with low-cost, flexible, and tunable organics that form a charge transfer (CT) heterojunction. As results, in-depth multifunctional CT 2D-inorganic/organic detector exhibits extended functions such as low-power consumption, in-memory detection, and optical-bio synapse to meet the demand of contemporary photonic cell. Particularly, the progresses in wafer-scale monocrystal of both 2D and organic materials render vast potential applications with operation frequencies ranging from ultraviolet to terahertz. Here, the recent advances of 2D-inorganic/organic CT photodetectors are comprehensively reviewed by several classifications. Future developments and applications in optical biology, synapsis, and machine vision are also highlighted. 

Recent Progress in 2D Inorganic/Organic Charge Transfer Heterojunction Photodetectors

Conventional computing based on von Neumann architecture cannot satisfy the demands of artificial intelligence (AI) applications anymore. Neuromorphic computing, emulating structures and principles based on the human brain, provides an alternative and promising approach for efficient and low consumption information processing. Herein, recent progress in neuromorphic computing enabled by emerging two-dimensional (2D) materials is introduced from devices design and hardware implementation to system integration. Especially, the advances of hopeful artificial synapses and neurons utilizing the resistive-switching-based devices, 2D ferroelectric-based memories and transistors, ultrafast flash, and promising transistors with attractive structures are highlighted. The device features, performance merits, bottlenecks, and possible improvement strategies, along with large-scale brain-inspired network fulfillment, are presented. Challenges and prospects of system application for neuromorphic computing are briefly discussed, shedding light on its great potential for AI.

Neuromorphic computing: Devices, hardware, and system application facilitated by two-dimensional materials

RNA modifications play critical roles in the regulation of various biological processes and are associated with many human diseases. Direct identification of RNA modifications by sequencing remains challenging, however. Nanopore sequencing is promising, but the current strategy is complicated by sequence decoding. Sequential nanopore identification of enzymatically cleaved nucleoside monophosphates may simultaneously provide accurate sequence and modification information. Here we show a phenylboronic acid-modified hetero-octameric Mycobacterium smegmatis porin A nanopore, with which direct distinguishing between monophosphates of canonical nucleosides, 5-methylcytidine, N6-methyladenosine, N7-methylguanosine, N1-methyladenosine, inosine, pseudouridine and dihydrouridine was achieved. A custom machine learning algorithm, which reports an accuracy of 0.996, was also applied to the quantitative analysis of modifications in microRNA and natural transfer RNA. It is generally suitable for sensing of a variety of other nucleoside or nucleotide derivatives and may bring new insights to epigenetic RNA sequencing. A phenylboronic acid-modified hetero-octameric Mycobacterium smegmatis porin A nanopore can directly distinguish 11 types of nucleoside monophosphates with a 0.996 accuracy. 

/pdf/identification-of-nucleoside-monophosphates-and-their-3p4k6gum.pdf

Identification of nucleoside monophosphates and their epigenetic modifications using an engineered nanopore

Saccharides play critical roles in many forms of cellular activities. Saccharide structures are however complicated and similar, setting a technical hurdle for direct identification. Nanopores, which are emerging single molecule tools sensitive to minor structural differences between analytes, can be engineered to identity saccharides. A hetero-octameric Mycobacterium smegmatis porin A nanopore containing a phenylboronic acid was prepared, and was able to clearly identify nine monosaccharide types, including D-Fructose, D-Galactose, D-Mannose, D-Glucose, L-Sorbose, D-Ribose, D-Xylose, L-Rhamnose and N-Acetyl-D-Galactosamine. Minor structural differences between saccharide epimers can also be distinguished. To assist automatic event classification, a machine learning algorithm was developed, with which a general accuracy score of 0.96 was achieved. This sensing strategy is generally suitable for other saccharide types and may bring new insights to nanopore saccharide sequencing.

A Nanopore-Based Saccharide Sensor.

Alditols, which have a sweet taste but produce much lower calories than natural sugars, are widely used as artificial sweeteners. Alditols are the reduced forms of monosaccharide aldoses, and different alditols are diastereomers or epimers of each other and direct and rapid identification by conventional methods is difficult. Nanopores, which are emerging single-molecule sensors with exceptional resolution when engineered appropriately, are useful for the recognition of diastereomers and epimers. In this work, direct distinguishing of alditols corresponding to all 15 monosaccharide aldoses was achieved by a boronic acid-appended hetero-octameric Mycobacterium smegmatis porin A (MspA) nanopore (MspA-PBA). Thirteen alditols including glycerol, erythritol, threitol, adonitol, arabitol, xylitol, mannitol, sorbitol, allitol, dulcitol, iditol, talitol, and gulitol (l-sorbitol) could be fully distinguished, and their sensing features constitute a complete nanopore alditol database. To automate event classification, a custom machine-learning algorithm was developed and delivered a 99.9% validation accuracy. This strategy was also used to identify alditol components in commercially available "zero-sugar" drinks and healthcare products, suggesting their use in rapid and sensitive quality control for the food and medical industry.

Zhenyuan Cao

Papers

Neuromorphic computing: Devices, hardware, and system application facilitated by two-dimensional materials

Identification of nucleoside monophosphates and their epigenetic modifications using an engineered nanopore

A Nanopore-Based Saccharide Sensor.

Nanopore Identification of Alditol Epimers and Their Application in Rapid Analysis of Alditol-Containing Drinks and Healthcare Products.

MSC-DNet: An efficient detector with multi-scale context for defect detection on strip steel surface