A stimulus-response system and conscious response enable humans to respond effectively to environmental changes and external stimuli. This paper presents an artificial stimulus-response system that is inspired by human conscious response and is capable of emulating it. The system is composed of an artificial visual receptor, artificial synapse, artificial neuron circuits, and actuator. By incorporating these artificial nervous components, a series of conscious response processes that markedly reduces response time as a result of learning from repeated stimuli are demonstrated. The proposed artificial stimulus-response system offers the promise of a new research field that would aid the development of artificial intelligence–based organs for patients with neurological disorders.

In this work, a highly retentive and synaptic-functional transistor memory device architecture based on the gate-deterministic remote doping of graphene via surface-oxidized Ti 3 C 2 T X MXene nano-floating-gates (NFG) is presented. By using solution-phase size-sorting followed by controlled surface oxidation process, a regulated distribution of MXene nanoflakes comprising metallic Ti 3 C 2 T X as the core surrounded by TiO 2 -a high dielectric constant insulator-as the shell is achieved. The size-sorted core/shell-like MXene nanoflakes show a self-sustainable charge trapping/detrapping behavior, which is highly feasible for realizing non-embed NFGs for transistor memory devices. Interestingly, unlike the conventional NFG-embedded architecture, the introduction of core/shell-like MXene under an electrolyte-gated graphene field-effect transistor (GFET) architecture induces a cooperative evolution of the hysteresis loop associated with ionic motion in the electrolyte gates and charge trapping/detrapping in the nanoflakes, resulting in a deterministic remote doping of the graphene layer. The resulting device exhibited a highly retentive memory behavior, which can be optimized by the nanoflake size distribution. In addition, synaptic functions having mechanical flexibility can be successfully emulated using MXene-based GFETs fabricated on a flexible polyethylene naphthalate substrate.

The advancement of electronic devices has enabled researchers to successfully emulate human synapses, thereby promoting the development of the research field of artificial synapse integrated soft robots. This paper proposes an artificial reciprocal inhibition system that can successfully emulate the human motor control mechanism through the integration of artificial synapses. The proposed system is composed of artificial synapses, load transistors, voltage/current amplifiers, and a soft actuator to demonstrate the muscle movement. The speed, range, and direction of the soft actuator movement can be precisely controlled via the preset input voltages with different amplitudes, numbers, and signs (positive or negative). The artificial reciprocal inhibition system can impart lifelike motion to soft robots and is a promising tool to enable the successful integration of soft robots or prostheses in a living body.

Multiplexing is essential for technologies that require processing of a large amount of information in real time. Here, we present an artificial synaptic multiplexing unit capable of realizing parallel multi-input control system. Ion gel was used as a dielectric layer of the artificial synaptic multiplexing unit because of its ionic property, allowing multigating for parallel input. A closed-loop control system that enables multi-input–based feedback for actuator bending control was realized by incorporating an ion gel–based artificial synaptic multiplexing unit, an actuator, and a bending angle sensor. The proposed multi-input control system could simultaneously process input and feedback signals, offering a breakthrough in industries in which the processing of vast amounts of streaming data is essential.

Extracting valuable information from the overflowing data is a critical yet challenging task. Dealing with high volumes of biometric data, which are often unstructured, nonstatic, and ambiguous, requires extensive computer resources and data specialists. Emerging neuromorphic computing technologies that mimic the data processing properties of biological neural networks offer a promising solution for handling overflowing data. Here, the development of an electrolyte-gated organic transistor featuring a selective transition from short-term to long-term plasticity of the biological synapse is presented. The memory behaviors of the synaptic device were precisely modulated by restricting ion penetration through an organic channel via photochemical reactions of the cross-linking molecules. Furthermore, the applicability of the memory-controlled synaptic device was verified by constructing a reconfigurable synaptic logic gate for implementing a medical algorithm without further weight-update process. Last, the presented neuromorphic device demonstrated feasibility to handle biometric information with various update periods and perform health care tasks.