A5100447640- Advanced Memory and Neural Computing
- Ferroelectric and Negative Capacitance Devices
- Advanced biosensing and bioanalysis techniques
- Semiconductor materials and devices
- Graphene research and applications
- Carbon Nanotubes in Composites
- Neuroscience and Neural Engineering
- Neural dynamics and brain function
- Biosensors and Analytical Detection
- Analytical Chemistry and Sensors
- Nanowire Synthesis and Applications
- Neural Networks and Reservoir Computing
- Transition Metal Oxide Nanomaterials
- Mechanical and Optical Resonators
- CCD and CMOS Imaging Sensors
- Educational Reforms and Innovations
- Advanced Sensor and Energy Harvesting Materials
- Molecular Junctions and Nanostructures
- Glycosylation and Glycoproteins Research
- SARS-CoV-2 detection and testing
- Plant Gene Expression Analysis
- Advanced Energy Technologies and Civil Engineering Innovations
- Pharmaceutical and Antibiotic Environmental Impacts
- Hygrothermal properties of building materials
- 3D IC and TSV technologies
Tsinghua University
2004-2024
China Tobacco
2024
Tobacco Research Institute
2024
Nankai University
2015-2022
University of Glasgow
2022
Institute of Microelectronics
2019-2021
Nagoya University
2021
Qingdao University
2021
Columbia University
2018-2019
State Key Joint Laboratory of Environment Simulation and Pollution Control
2016-2019
In this work, we report the monolithic three-dimensional integration (M3D) of hybrid memory architecture based on resistive random-access (RRAM), named M3D-LIME. The chip featured three key functional layers: first was Si complementary metal-oxide-semiconductor (CMOS) for control logic; second computing-in-memory (CIM) layer with HfAlOx-based analog RRAM array to implement neural networks feature extractions; third on-chip buffer and ternary content-addressable (TCAM) template storing...
A fully integrated graphene field‐effect transistor (GFET) nanosensor utilizing a novel high‐κ solid‐gating geometry for practical biosensor with enhanced sensitivity is presented. Herein, an “in plane” gate supplying electrical field through 30 nm HfO 2 dielectric layer employed to eliminate the cumbrous external wire electrode in conventional liquid‐gate GFET nanosensors that undesirably limits device potential on‐site sensing applications. In addition advantage integration degree,...
Herein, we demonstrate a plasmonic ELISA based on the alkaline phosphatase (ALP)-mediated growth of silver nanoparticles (AgNPs) for sensitive, rapid, and naked-eye detection cancer biomarkers in clinical serum samples. This approach was used to measure low-abundance alpha fetal protein (AFP) sera, which demonstrates its great capability differentiation cancers evaluation therapeutic responses. Impressively, readout assay depends rapid formation Ag colloidal solutions with various degrees...
Abstract Monochiral single‐walled carbon nanotubes (SWCNTs) are promising materials with potential applications in 3D integrated circuits and optoelectronic hybrid circuits. However, the purity device performance of monochiral SWCNTs still far lower than expected. Here, authors demonstrate that specific can be wrapped by conjugated polymers containing pyridine units, supramolecular assemblies show surprising suspension stability even after high‐intensity ultracentrifugation. Additionally,...
In the era of Internet Things, vast amounts data generated at sensory nodes impose critical challenges on data-transfer bandwidth and energy efficiency computing hardware. A near-sensor (NSC) architecture places processing units closer to sensors such that can be processed almost in situ with high efficiency. This study demonstrates monolithic three-dimensional (M3D) integration a photosensor array, analog computing-in-memory (CIM), Si complementary metal-oxide-semiconductor (CMOS) logic...
Plastic additive-related chemicals, particularly in polyvinyl chloride (PVC) plastics, have become a key issue plastic pollution. Although addressing pollution through the life-cycle approach is crucial, environmental impacts of typical chemicals PVC plastics during cradle-to-gate stage remain unexplored. Consequently, managing these additives remains challenging. Herein, 23 and six products were evaluated throughout using life cycle assessment-material flow analysis (LCA-MFA) coupled model....
We demonstrate a monolithic 3D integration of Si-based CMOS logic, resistive random-access memory (RRAM) based computing-in-memory (CIM) and ternary content-addressable (TCAM) layers, namely M3D-LIME, to implement one-shot learning. The first layer Si MOSFETs was designed fabricated using standard process served as control logic. second 1 T1R array with HfAlOx-based analog RRAM low-temperature (≤ 300°C) back-end-of-line (BEOL) CIM for feature extractions. third 2T2R-based TCAM carbon...
Abstract Resistive switching random‐access memory (RRAM) has attracted tremendous interest for applications in both embedded and neuromorphic computing. In this paper, two distinct types of resistive Ti/TiO x /Pd‐based RRAM devices depending on the bottom electrode morphology is reported. One filamentary where Pd spikes caused by liftoff. The enhancement local electric field induces formation conductive filaments hence leads device to be nonvolatile. other one dynamic switching, flat without...
Here we present a hybrid computing-in-memory (CIM) architecture, named M3D-CCP, by monolithically 3D integration of Si CMOS logic layer, RRAM-based CIM layer and processing-near-memory (PNM) with CNT/IGZO-based complementary field-effect transistor (CFET). The Si-CMOS was fabricated using standard 130 nm process served as control logic. consisted ITIR arrays analog resistive random-access memory (RRAM) for matrix-vector multiplication (MVM) operations in neural networks. CFET-based PNM...
Cinnamyl alcohol dehydrogenase (CAD) plays a crucial role in lignin biosynthesis, and the gene family encoding various CAD isozymes has been cloned characterized numerous plant species. However, limited information regarding tobacco is currently available. In this study, we identified 10 genes Nicotiana tabacum , four N. tomentosiformis six sylvestris . The nucleotide amino acid sequences of these CADs demonstrate high levels similarity, whereas putative protein conservatively possessed two...
In this work, we report a monolithically 3D integration of HfZrO <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">x</inf> (HZO) ferroelectric FET (FeFET), analog computing-in-memory (CIM), hybrid back-end-of-line (BEOL) CMOS on top standard Si-CMOS technology, namely M3D-FACT. The 1 <sup xmlns:xlink="http://www.w3.org/1999/xlink">st</sup> layer is Si circuits for control logic, and the 2 xmlns:xlink="http://www.w3.org/1999/xlink">nd</sup> an...
Computing-in-memory (CIM) based on analog resistive random access memory (RRAM) emerges as an energy-efficient technology for edge artificial intelligence (AI), where a large amount of ON-chip data buffer is needed to implement complex neural networks. In this work, we report novel InGaZnOx (IGZO)/carbon nanotube (CNT) hybrid-polarity 2T0C DRAM backend-of-the-line (BEOL) compatible buffer, which monolithic 3-D (M3D) integrated with HfO2-based RRAM array and Si CMOS logic demonstrate M3D-BRIC...
Resistive random access memory (RRAM) has been extensively studied for high-density and energy-efficient computing-in-memory (CIM) applications. In this work, the first time, we present a fully integrated 3-D stackable 1-kb one-CNTFET-one-RRAM (1T1R) array with carbon nanotube (CNT) CMOS peripheral circuits. The 1T1R cells were fabricated 1024 CNT NFETs Ta <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math...
Near-Sensor Computing In article number 2302658, Jianshi Tang and co-workers demonstrate a prototype M3D-SAIL chip for energy-efficient near-sensor computing by monolithic three-dimensional integration of an IGZO-FET based photosensor array, analog RRAM-based computing-in-memory Si CMOS logic circuits. A video keyframe-extraction task is implemented, achieving high classification accuracy 96.7% 31.5× lower energy consumption 1.91× faster speed compared to its 2D counterpart.
<title>Abstract</title> To fulfill complex human-machine interactions, a brain-computer interface (BCI) must not only decipher brain signals but also dynamically adapt to fluctuations, ultimately co-evolving with the brain. This necessitates novel decoder capable of flexible updates energy-efficient decoding capabilities. In this work, we designed co-evolutional BCI neuromorphic enabled by 128k-cell memristor chip. By interacting brain, continuously its parameters, leading successful...