A new type of atomically thin synaptic network on van der Waals (vdW) heterostructures is reported, where each ultrasmall cell (≈2 nm thick) built with trilayer WS₂ semiconductor acts as a gate-tunable photoactive synapse, i.e., a photo-memtransistor. A train of UV pulses onto the WS₂ memristor generates dopants in atomic-level precision by direct light-lattice interactions, which, along with the gate tunability, leads to the accurate modulation of the channel conductance for potentiation and depression of the synaptic cells. Such synaptic dynamics can be explained by a parallel atomistic resistor network model. In addition, it is shown that such a device scheme can generally be realized in other 2D vdW semiconductors, such as MoS₂ , MoSe₂ , MoTe₂ , and WSe₂ . Demonstration of these atomically thin photo-memtransistor arrays, where the synaptic weights can be tuned for the atomistic defect density, provides implications for a new type of artificial neural networks for parallel matrix computations with an ultrahigh integration density.

Owing in part to complementary metal-oxide-semiconductor (CMOS) scaling issues, the semiconductor industry is placing an increased emphasis on emerging materials and devices that may provide a solution beyond the 22-nm node. Single and few layers of carbon sheets (graphene) have been fabricated by a variety of techniques including mechanical exfoliation and chemical vapor deposition, and field-effect devices have been demonstrated with room temperature field-effect mobilities close to 10 000 cm 2 /Vs. But since graphene is a gapless semiconductor, these transistors have high off-state leakage and nonsaturating drive currents. This is problematic for digital logic, but is acceptable for analog device applications such as low-noise amplifiers and radio-frequency (RF)/millimeter-wave field-effect transistors (FETs). The remarkable transport physics of graphene due to its linear bandstructure have led to novel beyond CMOS logic devices as well, such as “pseudospin” devices.

Graphene has been attracting great interest because of its distinctive band structure and physical properties. Today, graphene is limited to small sizes because it is produced mostly by exfoliating graphite. We grew large-area graphene films of the order of centimeters on copper substrates by chemical vapor deposition using methane. The films are predominantly single-layer graphene, with a small percentage (less than 5%) of the area having few layers, and are continuous across copper surface steps and grain boundaries. The low solubility of carbon in copper appears to help make this growth process self-limiting. We also developed graphene film transfer processes to arbitrary substrates, and dual-gated field-effect transistors fabricated on silicon/silicon dioxide substrates showed electron mobilities as high as 4050 square centimeters per volt per second at room temperature.

Electrochemical random-access memory (ECRAM) devices are promising synaptic elements for neuromorphic computing due to their uniform and tunable programmability. However, despite growing interest in ECRAM devices, studies have primarily focused on devices with amorphous or polycrystalline tungsten oxide (WO₃) films, leaving the impact of single-crystalline materials in oxygen-based ECRAMs largely unexplored. This work reports the first realization of single-crystalline hexagonal tungsten oxide (h-WO₃) nanowire (NW) based ECRAM device. Leveraging the high crystallinity and distinct atomic structure of h-WO₃ NWs, the device exhibits significantly enhanced symmetry in conductance modulation, a key metric for synaptic emulation. More strikingly, a novel lateral switching mode emerges under specific configurations, accompanied by an unexpected conductance surge during relaxation, resembling neuronal integration and activation functions. A mechanism attributed to the electrode-NW interface properties is proposed to explain this behavior. These findings not only reveal a previously unexplored aspect of ECRAM switching physics, but also expand the functional potential of ECRAMs through fundamental material innovation.

Objectives This study aims to explore the experiences of undergraduate students majoring in counseling as they com plete interview assignments with practicing child counselors, using the Consensual Qualitative Research-Modified (CQR-M) method. The goal is to reveal the meaning of such field-based interview experiences in the professional de velopment of undergraduate counseling students. Methods Data were collected from 80 undergraduate students who participated in child counseling courses at B University over six semesters, from September 2020 to June 2023. The students submitted written reports after completing interviews with practicing child counselors. The data were analyzed using the CQR-M method, which involves iterative reading, individual coding, categorization, and researcher consensus. Results The analysis revealed four domains and 17 categories that reflect students’ experiences and perceived meanings of the interview assignment. First, through reflective experiences as prospective child counselors, students engaged in self-reflection and de veloped motivation and passion to grow as competent professionals. Second, through experiential learning about child counselors, they developed new perspectives on counselor qualities and expanded their understanding of roles and responsibilities grounded in real-world practice. Third, students gained experiential insight into child counseling, recognizing the uniqueness of working with child clients, the importance of parent counseling, and learning practical techniques involving play and art. Lastly, students began actively identifying concrete steps for career preparation and exploring future paths related to child counseling. Conclusions This study demonstrates that interview experiences with practicing child counselors significantly contribute to the development of professional competence and occupational identity among undergraduate coun seling students. The findings highlight the importance of integrating field-based experiences into counselor edu cation at the undergraduate level.

In neuromorphic computing, which overcomes the limitations of the von Neumann architecture, resistive processing units performing data processing and memory storage simultaneously have been widely researched. Among analog resistive devices, electrochemical random-access memories (ECRAMs) have drawn significant attention, which operate via ion movements to control channel conductance. Despite their promising switching characteristics, the contact resistance between Tungsten oxide (WO_x) channel and metal electrodes has not been studied yet. In this study, we fabricate transmission line model devices to investigate the contact resistance between WO_x and the metals of Tungsten (W), Molybdenum (Mo), and Nickel (Ni). Results show that W exhibits the lowest contact resistance with WO_x, followed by Ni and Mo. We confirm that W and Mo show Ohmic contacts, whereas Ni reveals a Schottky contact with a Schottky barrier height of 118 meV. Moreover, we demonstrate Mo only forms a metal oxidation layer of 9 nm at the interface between WO_x and Mo. Finally, we present the trend of contact resistivity depending on WO_x channel resistivity under different channel conductance values of the W contact. These findings provide insights into contact resistance of WO_x-based ECRAMs, suggesting that W is the most suitable contact material for achieving high performance.

Memristors have emerged as a key building block for artificial neural networks (ANNs), offering energy efficiency and high scalability for hardware-based synaptic weight updates. As device miniaturization is crucial for enhancing memristor performance, hexagonal boron nitride (h-BN) stands out as a promising resistive switching medium due to its excellent insulating characteristics even at an atomically thin scale. However, conventional h-BN memristors suffer from abrupt switching behavior by uncontrollable filament formation, limiting their potential for ANN applications. Here, h-BN-based memristors exhibiting linear and symmetric analog switching by leveraging multiple nano-filament confinement is presented. The geometric confinement between suspended h-BN films and the apexes of GaN nano-cones facilitates analog switching behavior, reducing cycle-to-cycle variation and ensuring stable consecutive operations. Electrical analyses reveal that analog switching behavior originates from the controlled formation of multiple nano-filaments within the confined geometry. ANNs implemented with these nano-filaments confined to h-BN memristors exhibit highly linear and symmetric synaptic weight updates, enabling precise training with minimal accuracy degradation. This work establishes multiple nano-filament confinement as a universal design strategy for achieving reliable and linear analog switching in memristors, paving the way for advanced neuromorphic computing.

A new type of atomically thin synaptic network on van der Waals (vdW) heterostructures is reported, where each ultrasmall cell (≈2 nm thick) built with trilayer WS₂ semiconductor acts as a gate-tunable photoactive synapse, i.e., a photo-memtransistor. A train of UV pulses onto the WS₂ memristor generates dopants in atomic-level precision by direct light-lattice interactions, which, along with the gate tunability, leads to the accurate modulation of the channel conductance for potentiation and depression of the synaptic cells. Such synaptic dynamics can be explained by a parallel atomistic resistor network model. In addition, it is shown that such a device scheme can generally be realized in other 2D vdW semiconductors, such as MoS₂ , MoSe₂ , MoTe₂ , and WSe₂ . Demonstration of these atomically thin photo-memtransistor arrays, where the synaptic weights can be tuned for the atomistic defect density, provides implications for a new type of artificial neural networks for parallel matrix computations with an ultrahigh integration density.

Owing in part to complementary metal-oxide-semiconductor (CMOS) scaling issues, the semiconductor industry is placing an increased emphasis on emerging materials and devices that may provide a solution beyond the 22-nm node. Single and few layers of carbon sheets (graphene) have been fabricated by a variety of techniques including mechanical exfoliation and chemical vapor deposition, and field-effect devices have been demonstrated with room temperature field-effect mobilities close to 10 000 cm 2 /Vs. But since graphene is a gapless semiconductor, these transistors have high off-state leakage and nonsaturating drive currents. This is problematic for digital logic, but is acceptable for analog device applications such as low-noise amplifiers and radio-frequency (RF)/millimeter-wave field-effect transistors (FETs). The remarkable transport physics of graphene due to its linear bandstructure have led to novel beyond CMOS logic devices as well, such as “pseudospin” devices.