Topological Quantum Switching Enabled Neuroelectronic Synaptic Modulators for Brain Computer Interface.

Aging and genetic-related disorders in the human brain lead to impairment of daily cognitive functions. Due to their neural synaptic complexity and the current limits of knowledge, reversing these disorders remains a substantial challenge for brain-computer interfaces (BCI). In this work, a solution is provided to potentially override aging and neurological disorder-related cognitive function loss in the human brain through the application of the authors' quantum synaptic device. To illustrate this point, a quantum topological insulator (QTI) Bi2Se2Te-based synaptic neuroelectronic device, where the electric field-induced tunable topological surface edge states and quantum switching properties make them a premier option for establishing artificial synaptic neuromodulation approaches, is designed and developed. Leveraging these unique quantum synaptic properties, the developed synaptic device provides the capability to neuromodulate distorted neural signals, leading to the reversal of age-related disorders via BCI. With the synaptic neuroelectronic characteristics of this device, excellent efficacy in treating cognitive neural dysfunctions through modulated neuromorphic stimuli is demonstrated. As a proof of concept, real-time neuromodulation of electroencephalogram (EEG) deduced distorted event-related potentials (ERP) is demonstrated by modulation of the synaptic device array.

Metal-Induced Trap States: The Roles of Interface and Border Traps in HfO2/InGaAs.

By combining capacitance-voltage measurements, TCAD simulations, and X-ray photoelectron spectroscopy, the impact of the work function of the gate metals Ti, Mo, Pd, and Ni on the defects in bulk HfO2 and at the HfO2/InGaAs interfaces are studied. The oxidation at Ti/HfO2 is found to create the highest density of interface and border traps, while a stable interface at the Mo/HfO2 interface leads to the smallest density of traps in our sample. The extracted values of Dit of 1.27 × 1011 eV-1cm-2 for acceptor-like traps and 3.81 × 1011 eV-1cm-2 for donor-like traps are the lowest reported to date. The density and lifetimes of border traps in HfO2 are examined using the Heiman function and strongly affect the hysteresis of capacitance-voltage curves. The results help systematically guide the choice of gate metal for InGaAs.

Impact of an Underlying 2DEG on the Performance of a p-Channel MOSFET in GaN.

The influence of an underlying 2-dimensional electron gas (2DEG) on the performance of a normally off p-type metal oxide semiconductor field effect transistor (MOSFET) based on GaN/AlGaN/GaN double heterojunction is analyzed via simulations. By reducing the concentration of the 2DEG, a greater potential can be dropped across the GaN channel, resulting in enhanced electrostatic control. Therefore, to minimize the deleterious impact on the on-state performance, a composite graded back-to-back AlGaN barrier that enables a trade-off between n-channel devices and Enhancement-mode (E-mode) p-channel is investigated. In simulations, a scaled p-channel GaN device with LG = 200 nm, LSD = 600 nm achieves an ION of 65 mA/mm, an increase of 44.4% compared to a device with an AlGaN barrier with fixed Al mole fraction, ION/IOFF of ∼1012, and |Vth| of | - 1.3 V|. For the n-channel device, the back-to-back barrier overcomes the reduction of ION induced by the p-GaN gate resulting in an ION of 860 mA/mm, an increase of 19.7% compared with the counterpart with the conventional barrier with 0.5 V positive Vth shift.

Quantum Topological Neuristors for Advanced Neuromorphic Intelligent Systems.

Neuromorphic artificial intelligence systems are the future of ultrahigh performance computing clusters to overcome complex scientific and economical challenges. Despite their importance, the advancement in quantum neuromorphic systems is slow without specific device design. To elucidate biomimicking mammalian brain synapses, a new class of quantum topological neuristors (QTN) with ultralow energy consumption (pJ) and higher switching speed (µs) is introduced. Bioinspired neural network characteristics of QTNs are the effects of edge state transport and tunable energy gap in the quantum topological insulator (QTI) materials. With augmented device and QTI material design, top notch neuromorphic behavior with effective learning-relearning-forgetting stages is demonstrated. Critically, to emulate the real-time neuromorphic efficiency, training of the QTNs is demonstrated with simple hand gesture game by interfacing them with artificial neural networks to perform decision-making operations. Strategically, the QTNs prove the possession of incomparable potential to realize next-gen neuromorphic computing for the development of intelligent machines and humanoids.

Origins of the Schottky Barrier to a 2DHG in a Au/Ni/GaN/AlGaN/GaN Heterostructure.

We report the influence of thickness of an undoped GaN (u-GaN) layer on current transport to a 2DHG through the metal/p++GaN contact in a GaN/AlGaN/GaN heterostructure. The current is dominated by an internal potential barrier of 0.2-0.27 eV at the p+ GaN/u-GaN, which increases with thickness from 5 to 15 nm and remains constant thereafter due to Fermi pinning by a defect at ∼0.6 eV from the top valence band. We also report a nonideality factor, n, between 6 and 12, for the combined tunneling current through the p+GaN/u-GaN to the 2DHG. Our contact resistivity of 5.3 × 10-4 Ω cm2 and hole mobility, µ, of ∼15.65 cm2/V s are the best-in-class for this metal stack on a GaN/AlGaN/GaN heterostructure, reported to date.

Negative Capacitance beyond Ferroelectric Switches.

Negative capacitance transistors are a unique class of switches capable of operation beyond the Boltzmann limit to realize subthermionic switching. To date, the negative capacitance effect has been predominantly attributed to devices employing an unstable insulator with ferroelectric properties, exhibiting a two-well energy landscape, in accordance with the Landau theory. The theory and operation of a solid electrolyte field effect transistor (SE-FET) of subthreshold swing less than 60 mV/dec in the absence of a ferroelectric gate dielectric are demonstrated in this work. Unlike ferroelectric FETs that rely on a sudden switching of dipoles to achieve negative capacitance, we demonstrate a distinctive mechanism that relies on the accumulation and dispersion of ions at the interfaces of the oxide, leading to a subthreshold slope (SS) as low as 26 mV/dec in these samples. The frequency of operation of these unscaled devices lies in a few millihertz because at higher or lower frequencies, the ions in the insulator are either too fast or too slow to produce voltage amplification. This is unlike Landau switches, where the SS remains below 60 mV/dec even under quasi-static sweep of the gate bias. The proposed FETs show a higher on-current with a thicker oxide in the entire range of gate voltage, clearly distinguishing their scaling laws from those of ferroelectric FETs. Our theory, validated with experiment, demonstrates a new class of devices capable of negative capacitance that opens up alternate methods of steep switching beyond the traditional approach of ferroelectric or memristive FETs.

Diffusion-Controlled Faradaic Charge Storage in High-Performance Solid Electrolyte-Gated Zinc Oxide Thin-Film Transistors.

An electrochemical device capable of manifesting reversible charge storage at the interface of an active layer offers formidable advantages, such as low switching energy and long retention time, in realizing synaptic behavior for ultralow power neuromorphic systems. Contrary to a supercapacitor-based field-effect device that is prone to low memory retention due to fast discharge, a solid electrolyte-gated ZnO thin-film device exhibiting a battery-controlled charge storage mechanism via mobile charges at its interface with tantalum oxide is demonstrated. Analysis via cyclic voltammetry and chronoamperometry uniquely distinguishes the battery behavior of these devices, with an electromotive force generated due to polarization of charges strongly dependent on the scan rate of the applied voltage. The Faradaic-type diffusion-controlled charge storage mechanism exhibited by these devices is capable of delivering robust enhancement in the channel conductance and leads to a superior ON-OFF ratio of 108-109. The nonvolatile behavior of the interface charge storage and slow diffusion of ions is utilized in efficiently emulating spike timing-dependent plasticity (STDP) at similar time scales of biological synapses and unveils the possibility of STDP behavior using multiple in-plane gates that alleviate additional requirement of waveform-shaping circuits.

Nanoionics-Based Three-Terminal Synaptic Device Using Zinc Oxide.

Artificial synaptic thin film transistors (TFTs) capable of simultaneously manifesting signal transmission and self-learning are demonstrated using transparent zinc oxide (ZnO) in combination with high κ tantalum oxide as gate insulator. The devices exhibit pronounced memory retention with a memory window in excess of 4 V realized using an operating voltage less than 6 V. Gate polarity induced motion of oxygen vacancies in the gate insulator is proposed to play a vital role in emulating synaptic behavior, directly measured as the transmission of a signal between the source and drain (S/D) terminals, but with the added benefit of independent control of synaptic weight. Unlike in two terminal memristor/resistive switching devices, multistate memory levels are demonstrated using the gate terminal without hampering the signal transmission across the S/D electrodes. Synaptic functions in the devices can be emulated using a low programming voltage of 200 mV, an order of magnitude smaller than in conventional resistive random access memory and other field effect transistor based synaptic technologies. Robust synaptic properties demonstrated using fully transparent, ecofriendly inorganic materials chosen here show greater promise in realizing scalable synaptic devices compared to organic synaptic and other liquid electrolyte gated device technologies. Most importantly, the strong coupling between the in-plane gate and semiconductor channel through ionic charge in the gate insulator shown by these devices, can lead to an artificial neural network with multiple presynaptic terminals for complex synaptic learning processes. This provides opportunities to alleviate the extreme requirements of component and interconnect density in realizing brainlike systems.