Ambipolar dual-gate transistors based on low-dimensional materials, such as graphene, carbon nanotubes, black phosphorus, and certain transition metal dichalcogenides (TMDs), enable reconfigurable ...logic circuits with a suppressed off-state current. These circuits achieve the same logical output as complementary metal-oxide semiconductor (CMOS) with fewer transistors and offer greater flexibility in design. The primary challenge lies in the cascadability and power consumption of these logic gates with static CMOS-like connections. In this article, high-performance ambipolar dual-gate transistors based on tungsten diselenide (WSe
) are fabricated. A high on-off ratio of 10
and 10
, a low off-state current of 100 to 300 fA, a negligible hysteresis, and an ideal subthreshold swing of 62 and 63 mV/dec are measured in the p- and n-type transport, respectively. We demonstrate cascadable and cascaded logic gates using ambipolar TMD transistors with minimal static power consumption, including inverters, XOR, NAND, NOR, and buffers made by cascaded inverters. A thorough study of both the control gate and the polarity gate behavior is conducted. The noise margin of the logic gates is measured and analyzed. The large noise margin enables the implementation of V
-drop circuits, a type of logic with reduced transistor number and simplified circuit design. Finally, the speed performance of the V
-drop and other circuits built by dual-gate devices is qualitatively analyzed. This work makes advancements in the field of ambipolar dual-gate TMD transistors, showing their potential for low-power, high-speed, and more flexible logic circuits.
Ambipolar carbon nanotube based field-effect transistors (AP-CNFETs) exhibit unique electrical characteristics, such as tri-state operation and bi-directionality, enabling systems with complex and ...reconfigurable computing. In this paper, AP-CNFETs are used to design a mixed-signal machine learning logistic regression classifier. The classifier is designed in SPICE with feature size of 15 nm and operates at 250 MHz. The system is demonstrated in SPICE based on MNIST digit dataset, yielding 90% accuracy and no accuracy degradation as compared with the classification of this dataset in Python. The system also exhibits lower power consumption and smaller physical size as compared with the state-of-the-art CMOS and memristor based mixed-signal classifiers.
The spin-transfer torque domain wall (DW) magnetic tunnel junction (MTJ) enables spintronic logic circuits that can be directly cascaded without deleterious signal conversion circuitry and is one of ...the only spintronic devices for which cascading has been demonstrated experimentally. However, experimental progress has been impeded by a cumbersome modeling technique that requires a combination of micromagnetic and SPICE simulations. This paper, therefore, presents a SPICE-only device model that efficiently determines the DW motion resulting from spin accumulation and calculates the corresponding MTJ resistance. This model has been validated through comparison to the authoritative micromagnetic-based model, enabling reliable prediction of circuit behavior as a function of device parameters with a 10 000<inline-formula> <tex-math notation="LaTeX">\times </tex-math></inline-formula> reduction in the simulation time. This model thus enables deeper device and circuit investigation, advancing the prospects for nonvolatile spintronic computing systems that overcome the von Neumann bottleneck.
Emerging materials and physics can be leveraged for new device-inherent behavior that can have system-level benefits. Motivation for device, circuit, and system behavior can be drawn from how the ...human brain processes certain data-intensive tasks adaptively and quickly, such as canonical image recognition. The field of neuromorphic computing has made great strides in implementing multi-weight synaptic behavior, as well as neuronal behavior such as integrate-and-fire and stochastic switching, and implementation of such behaviors in deep neural network (DNN) processing. Using CMOS, emerging resistive memories, and other device types as the basis, neuromorphic computing is innovating vertically from devices, to circuits, to systems, to redefine how computation can be done.
Photothermal therapy (PTT) and magnetic hyperthermia therapy (MHT) using 2D nanomaterials (2DnMat) have recently emerged as promising alternative treatments for cancer and bacterial infections, both ...important global health challenges. The present review intends to provide not only a comprehensive overview, but also an integrative approach of the state‐of‐the‐art knowledge on 2DnMat for PTT and MHT of cancer and infections. High surface area, high extinction coefficient in near‐infra‐red (NIR) region, responsiveness to external stimuli like magnetic fields, and the endless possibilities of surface functionalization, make 2DnMat ideal platforms for PTT and MHT. Most of these materials are biocompatible with mammalian cells, presenting some cytotoxicity against bacteria. However, each material must be comprehensively characterized physiochemically and biologically, since small variations can have significant biological impact. Highly efficient and selective in vitro and in vivo PTTs for the treatment of cancer and infections are reported, using a wide range of 2DnMat concentrations and incubation times. MHT is described to be more effective against bacterial infections than against cancer therapy. Despite the promising results attained, some challenges remain, such as improving 2DnMat conjugation with drugs, understanding their in vivo biodegradation, and refining the evaluation criteria to measure PTT or MHT effects.
Graphene‐based materials (GBM), transition metal dichalcogenide (TMDC), transition metal oxide (TMO), MXenes, and black phosphorus (BP) surge as new 2DnMat for cancer/infections treatment. 2DnMat + NIR kills cancer cells/bacteria through hyperthermia in vitro and in vivo. Magnetic hyperthermia therapy using magnetic 2DnMat causes cancer or bacteria death. 2DnMat can be conjugated with molecules via covalent or non‐covalent interactions. Conjugation with drugs or polymers increase biocompatibility and therapeutic effect.
Hybrid Pass Transistor Logic With Ambipolar Transistors Hu, Xuan; Abraham, Amy S.; Incorvia, Jean Anne C. ...
IEEE transactions on circuits and systems. I, Regular papers,
2021-Jan., 2021-1-00, 20210101, Letnik:
68, Številka:
1
Journal Article
Recenzirano
Odprti dostop
The pass transistor logic (PTL) family enables compact circuits to reduce area and power consumption, but inter-stage inverters are required for signal integrity and complementary signals. Similarly, ...dual-gate ambipolar field-effect transistors are exceptionally logically expressive and provide a single-transistor XNOR operation, but numerous inverters are required to provide complementary signals. In both cases, these inverters and complementary signals significantly degrade overall system efficiency. Ambipolar field-effect transistors are a natural match for PTL, and we therefore propose a new hybrid ambipolar-PTL logic family that exploits the compact logic of PTL and the ambipolar capabilities of ambipolar field-effect transistors. This logic family is a hybrid between PTL and static CMOS-like logic that is made efficient by the use of ambipolar transistors. Novel hybrid ambipolar-PTL circuits were designed and simulated in SPICE, demonstrating strong signal integrity along with the efficiency advantages of using the required inverters to simultaneously satisfy the requirements of PTL and ambipolar circuits. In comparison to the ambipolar field-effect transistors in the conventional static CMOS logic structure, the proposed ambipolar-PTL family can reduce propagation delay by 33%, energy consumption by 88%, energy-delay product by a factor of 10, and area-energy-delay product by a factor greater than 20.
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