| 1. |
- Yin, X., et al.
(författare)
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Vertical Sandwich Gate-All-Around Field-Effect Transistors with Self-Aligned High-k Metal Gates and Small Effective-Gate-Length Variation
- 2020
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Ingår i: IEEE Electron Device Letters. - : Institute of Electrical and Electronics Engineers Inc.. - 0741-3106 .- 1558-0563. ; 41:1, s. 8-11
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Tidskriftsartikel (refereegranskat)abstract
- A new type of vertical nanowire (NW)/nanosheet (NS) field-effect transistors (FETs), termed vertical sandwich gate-all-around (GAA) FETs (VSAFETs), is presented in this work. Moreover, an integration flow that is compatible with processes used in the mainstream industry is proposed for the VSAFETs. Si/SiGe epitaxy, isotropic quasi-atomic-layer etching (qALE), and gate replacement were used to fabricate pVSAFETs for the first time. Vertical GAA FETs with self-aligned high-k metal gates and a small effective-gate-length variation were obtained. Isotropic qALE, including Si-selective etching of SiGe, was developed to control the diameter/thickness of the NW/NS channels. NWs with a diameter of 10 nm and NSs with a thickness of 20 nm were successfully fabricated, and good device characteristics were obtained. Finally, the device performance was investigated and is discussed in this work. © 2019 IEEE.
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| 2. |
- Abedin, Sarder, et al.
(författare)
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Data Freshness and Energy-Efficient UAV Navigation Optimization : A Deep Reinforcement Learning Approach
- 2021
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Ingår i: IEEE transactions on intelligent transportation systems (Print). - 1524-9050 .- 1558-0016. ; 22:9, s. 5994-6006
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Tidskriftsartikel (refereegranskat)abstract
- In this paper, we design a navigation policy for multiple unmanned aerial vehicles (UAVs) where mobile base stations (BSs) are deployed to improve the data freshness and connectivity to the Internet of Things (IoT) devices. First, we formulate an energy-efficient trajectory optimization problem in which the objective is to maximize the energy efficiency by optimizing the UAV-BS trajectory policy. We also incorporate different contextual information such as energy and age of information (AoI) constraints to ensure the data freshness at the ground BS. Second, we propose an agile deep reinforcement learning with experience replay model to solve the formulated problem concerning the contextual constraints for the UAV-BS navigation. Moreover, the proposed approach is well-suited for solving the problem, since the state space of the problem is extremely large and finding the best trajectory policy with useful contextual features is too complex for the UAV-BSs. By applying the proposed trained model, an effective real-time trajectory policy for the UAV-BSs captures the observable network states over time. Finally, the simulation results illustrate the proposed approach is 3.6 % and 3.13 % more energy efficient than those of the greedy and baseline deep Q Network (DQN) approaches.
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| 3. |
- Li, J., et al.
(författare)
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Study of selective isotropic etching Si1−xGex in process of nanowire transistors
- 2020
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Ingår i: Journal of materials science. Materials in electronics. - : Springer Science and Business Media LLC. - 0957-4522 .- 1573-482X. ; 31:1, s. 134-143
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Tidskriftsartikel (refereegranskat)abstract
- On approach towards the end of technology roadmap, a revolutionary approach towards the nanowire transistors is favorable due to the full control of carrier transport. The transistor design moves toward vertically or laterally stacked Gate-All-Around (GAA) where Si or SiGe can be used as channel material. This study presents a novel isotropic inductively coupled plasma (ICP) dry etching of Si1−xGex (0.10 ≤ x ≤ 0.28) in SiGe/Si multilayer structures (MLSs) with high selectivity to Si, SiO2, Si3N4 and SiON which can be applied in advanced 3D transistors and Micro-Electro-Mechanical System (MEMS) in future. The profile of SiGe etching for different thicknesses, compositions and locations in MLSs using dry or wet etch have been studied. A special care has been spent for layer quality of Si, strain relaxation of SiGe layers as well as residual contamination during the etching. In difference with dry etching methods (downstream remote plasma), the conventional ICP source in situ is used where CF4/O2/He gas mixture was used as the etching gas to obtain higher selectivity. Based on the reliability of ICP technique a range of etching rate 25–50 nm/min can be obtained for accurate isotropic etching of Si1−xGex, to form cavity in advanced 3D transistor processes in future.
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| 4. |
- Li, J., et al.
(författare)
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Study of silicon nitride inner spacer formation in process of gate-all-around nano-transistors
- 2020
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Ingår i: Nanomaterials. - : MDPI AG. - 2079-4991. ; 10:4
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Tidskriftsartikel (refereegranskat)abstract
- Stacked SiGe/Si structures are widely used as the units for gate-all-around nanowire transistors (GAA NWTs) which are a promising candidate beyond fin field effective transistors (FinFETs) technologies in near future. These structures deal with a several challenges brought by the shrinking of device dimensions. The preparation of inner spacers is one of the most critical processes for GAA nano-scale transistors. This study focuses on two key processes: Inner spacer film conformal deposition and accurate etching. The results show that low pressure chemical vapor deposition (LPCVD) silicon nitride has a good film filling effect; a precise and controllable silicon nitride inner spacer structure is prepared by using an inductively coupled plasma (ICP) tool and a new gas mixtures of CH2F2/CH4/O2/Ar. Silicon nitride inner spacer etch has a high etch selectivity ratio, exceeding 100:1 to Si and more than 30:1 to SiO2. High anisotropy with an excellent vertical/lateral etch ratio exceeding 80:1 is successfully demonstrated. It also provides a solution to the key process challenges of nano-transistors beyond 5 nm node. © 2020 by the authors. Licensee MDPI, Basel, Switzerland.
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| 5. |
- Munir, M. S., et al.
(författare)
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Risk-Aware Energy Scheduling for Edge Computing with Microgrid : A Multi-Agent Deep Reinforcement Learning Approach
- 2021
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Ingår i: IEEE Transactions on Network and Service Management. - 1932-4537. ; 18:3, s. 3476-3497
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Tidskriftsartikel (refereegranskat)abstract
- In recent years, multi-access edge computing (MEC) is a key enabler for handling the massive expansion of Internet of Things (IoT) applications and services. However, energy consumption of a MEC network depends on volatile tasks that induces risk for energy demand estimations. As an energy supplier, a microgrid can facilitate seamless energy supply. However, the risk associated with energy supply is also increased due to unpredictable energy generation from renewable and non-renewable sources. Especially, the risk of energy shortfall is involved with uncertainties in both energy consumption and generation. In this paper, we study a risk-aware energy scheduling problem for a microgrid-powered MEC network. First, we formulate an optimization problem considering the conditional value-at-risk (CVaR) measurement for both energy consumption and generation, where the objective is to minimize the expected residual of scheduled energy for the MEC networks and we show this problem is an NP-hard problem. Second, we analyze our formulated problem using a multi-agent stochastic game that ensures the joint policy Nash equilibrium, and show the convergence of the proposed model. Third, we derive the solution by applying a multi-agent deep reinforcement learning (MADRL)-based asynchronous advantage actor-critic (A3C) algorithm with shared neural networks. This method mitigates the curse of dimensionality of the state space and chooses the best policy among the agents for the proposed problem. Finally, the experimental results establish a significant performance gain by considering CVaR for high accuracy energy scheduling of the proposed model than both the single and random agent models.
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