NAND and NOR logic-in-memory comprising silicon nanowire feedback field-effect transistors

Yang, Young Soon; Jeon, Juhee; Son, Jaemin; Cho, Kyoungah; Kim, Sangsig

doi:10.1038/s41598-022-07368-0

Cited by 7 publications

(7 citation statements)

References 42 publications

(33 reference statements)

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“…Deep learning has become the mainstream of machine learning, and with the expansion of its applications, technical problems from the perspective of real-world problem solving have also emerged, primarily the black box problem: neural network machinelearning in deep learning is a black box [130][131][132][133][134][135][136][137][138][139][140][141][142][143][144]. The learning results are reflected in the node weights, and the obtained regularities and models are not represented in a form that humans can directly understand.…”

Section: Black Box Problemmentioning

confidence: 99%

Applications of Deep Learning for Drug Discovery Systems with BigData

Matsuzaka

Yashiro

2022

BioMedInformatics

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The adoption of “artificial intelligence (AI) in drug discovery”, where AI is used in the process of pharmaceutical research and development, is progressing. By using the ability to process large amounts of data, which is a characteristic of AI, and achieving advanced data analysis and inference, there are benefits such as shortening development time, reducing costs, and reducing the workload of researchers. There are various problems in drug development, but the following two issues are particularly problematic: (1) the yearly increases in development time and cost of drugs and (2) the difficulty in finding highly accurate target genes. Therefore, screening and simulation using AI are expected. Researchers have high demands for data collection and the utilization of infrastructure for AI analysis. In the field of drug discovery, for example, interest in data use increases with the amount of chemical or biological data available. The application of AI in drug discovery is becoming more active due to improvement in computer processing power and the development and spread of machine-learning frameworks, including deep learning. To evaluate performance, various statistical indices have been introduced. However, the factors affected in performance have not been revealed completely. In this study, we summarized and reviewed the applications of deep learning for drug discovery with BigData.

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Section: Black Box Problemmentioning

confidence: 99%

Applications of Deep Learning for Drug Discovery Systems with BigData

Matsuzaka

Yashiro

2022

BioMedInformatics

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“…[12][13][14][15][16] Recently, feedback field-effect transistors (FBFETs) that operate via positive feedback (PF) loop mechanisms have attracted DOI: 10.1002/aelm.202300132 attention for LIM. [17][18][19][20] PF loops are generated or eliminated in the channels because of the recursive mutual interaction between the carrier injection and potential barriers. This enables the transistors to feature steep switching and memory characteristics.…”

Section: Introductionmentioning

confidence: 99%

Logic‐In‐Memory Characteristics of Reconfigurable Feedback Field‐Effect Transistors with Double‐Gated Structure

Shin

Son

Jeon

et al. 2023

Adv Elect Materials

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The reconfigurable feedback field‐effect transistors (R‐FBFETs) with a double‐gated structure are designed and the logic and memory operations of a logic‐in‐memory (LIM) inverter comprising two R‐FBFETs are investigated. The R‐FBFETs exhibit an extremely low subthreshold swing of ≈1 mV dec−1, a high on/off current ratio of ≈107, and a long retention time of 10 s, owing to a positive feedback loop mechanism. The on‐current ratio of the p‐ to n‐channel modes is 1.03, which indicates a high degree of reconfigurability. The LIM inverter retains the output logic “1” and “0” states for over 50 s under zero‐bias conditions. The symmetric reconfigurable switching and memory operations of the R‐FBFETs enable the LIM inverter to perform logic and memory operations for a long retention time without a power supply.

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“…[14][15][16][17] FBFETs with a configuration of complementary metaloxide-semiconductor logic gates perform inverting and NAND/NOR logic operations and they memorize their logic outputs. [18][19][20][21][22] Moreover, FBFETs can also be utilized for reconfigurable logic gates with multi-gated structures. These devices can be reconfigured in n-channel or p-channel modes; electrostatic doping induced by gate bias enables the reconfiguration.…”

Section: Introductionmentioning

confidence: 99%

Reconfigurable Logic‐In‐Memory Cell Comprising Triple‐Gated Feedback Field‐Effect Transistors

Han,

Son,

Jeon

et al. 2023

Adv Elect Materials

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A reconfigurable logic‐in‐memory (R‐LIM) cell performs logic‐in‐memory functions as well as reconfigurable logic gates. The R‐LIM cell is constructed with triple‐gated (TG) feedback field‐effect transistors (FBFETs) that are reconfigured in n‐channel or p‐channel modes via electrostatic doping. Each TG FBFET has one control gate electrode and two program‐gate electrodes that determine the channel mode. Their reconfigurability enables the symmetrical operation of the n‐channel and p‐channel modes through an on‐current ratio of 1:04. Furthermore, the R‐LIM cell performs eight Boolean logic operations, storing the logic outputs for ≈100 s under zero‐bias conditions. The R‐LIM cell is useful for developing in‐memory computing systems with high energy efficiency and functional logic.

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NAND and NOR logic-in-memory comprising silicon nanowire feedback field-effect transistors

Cited by 7 publications

References 42 publications

Applications of Deep Learning for Drug Discovery Systems with BigData

Applications of Deep Learning for Drug Discovery Systems with BigData

Logic‐In‐Memory Characteristics of Reconfigurable Feedback Field‐Effect Transistors with Double‐Gated Structure

Reconfigurable Logic‐In‐Memory Cell Comprising Triple‐Gated Feedback Field‐Effect Transistors

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