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Related Concept Videos

Network Function of a Circuit01:25

Network Function of a Circuit

Frequency response analysis in electrical circuits provides vital insights into a circuit's behavior as the frequency of the input signal changes. The transfer function, a mathematical tool, is instrumental in understanding this behavior. It defines the relationship between phasor output and input and comes in four types: voltage gain, current gain, transfer impedance, and transfer admittance. The critical components of the transfer function are the poles and zeros.
Bipolar Junction Transistor01:22

Bipolar Junction Transistor

Bipolar Junction Transistors (BJTs) are essential elements in electronic circuits, playing a crucial role in the functionality of amplifiers, memories, and microprocessors. These transistors can be designed as NPN or PNP based on their doping patterns. They consist of three layers: the emitter, base, and collector. The configuration of these layers and their respective doping levels—with N-type or P-type impurities—define the transistor's type and its operational characteristics.
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Neural Circuits01:25

Neural Circuits

Neural circuits and neuronal pools are two of the main structures found in the nervous system. Neural circuits are networks of neurons that work together to carry out a specific task or process. They consist of interconnected neurons and glial cells, which provide structural and metabolic support.
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P-N junction01:11

P-N junction

A p-n junction is formed when p-type and n-type semiconductor materials are joined together. At the interface of the p-n junction, holes from the p-side and electrons from the n-side begin to diffuse into the opposite sides due to the concentration gradient. This diffusion of carriers leads to a region around the junction where there are no free charge carriers, known as the depletion region. The charge density within the depletion region for the n-side and p-side can be described by the...
MOSFET: Enhancement Mode01:22

MOSFET: Enhancement Mode

Enhancement-mode MOSFETs are pivotal components in electronics, distinguished by their capacity to act as highly efficient switches. They are part of the larger family of metal-oxide Semiconductor Field-Effect Transistors (MOSFETs). They are available in two types: p-channel and n-channel, each tailored to specific polarity operations.
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The Neuromuscular Junction

The nervous system consists of complex motor neuron circuits, including upper motor neurons originating from the cerebral cortex and lower motor neurons starting in the spinal cord, coordinating both voluntary and involuntary movements. Among these, somatic motor neurons activate skeletal muscles and are classified into alpha, beta, and gamma types. Alpha neurons are vital for voluntary movement coordination, while gamma neurons adjust muscle spindle sensitivity, and the function of beta...

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Related Experiment Video

Updated: May 31, 2026

Recording and Analyzing Multimodal Large-Scale Neuronal Ensemble Dynamics on CMOS-Integrated High-Density Microelectrode Array
09:44

Recording and Analyzing Multimodal Large-Scale Neuronal Ensemble Dynamics on CMOS-Integrated High-Density Microelectrode Array

Published on: March 8, 2024

TiON/NiOx Heterojunction Neuron for CMOS-Compatible Hardware Implementation of Activation Function in Neural Network.

Sion Kim1, Minsu Kang1, Yuna Kim1

  • 1Department of Semiconductor Engineering, Kwangwoon University, Seoul 01897, Korea.

ACS Applied Materials & Interfaces
|May 29, 2026
PubMed
Summary

A novel PN heterojunction neuron device enables hardware-level deep neural networks (HDNNs) with enhanced integration. This compact neuron mimics biological function, offering a scalable alternative to complementary metal-oxide-semiconductor (CMOS) circuits for efficient AI hardware.

Keywords:
3D vertical integrationCMOS-compatible hardwarePN heterojunction neuronhardware deep neural networkneuromorphic computingrectified linear unit

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Recording and Analyzing Multimodal Large-Scale Neuronal Ensemble Dynamics on CMOS-Integrated High-Density Microelectrode Array
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Published on: March 8, 2024

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Area of Science:

  • Solid State Physics
  • Materials Science
  • Neuro-inspired Computing

Background:

  • Current complementary metal-oxide-semiconductor (CMOS) neuron circuits face limitations in integration density for hardware-level deep neural networks (HDNNs).
  • Developing compact, efficient neuron devices is crucial for advancing AI hardware capabilities.

Purpose of the Study:

  • To introduce and characterize a novel PN heterojunction neuron device for HDNNs.
  • To investigate the physical origins of its intrinsic thresholding behavior.
  • To demonstrate its functionality and compatibility with multilayer HDNN architectures.

Main Methods:

  • Systematic analysis of the PN heterojunction's energy-band structure, diode parameters, and conduction mechanisms.
  • Experimental characterization of rectification and nonlinear activation properties, including threshold voltage (Vth) and rectification ratio (RR).
  • Integration with synapse devices to process summed currents and with transistors for multilayer signal propagation.

Main Results:

  • The PN heterojunction neuron exhibits rectification and nonlinear activation, implementing the rectified linear unit (ReLU) function intrinsically.
  • Key parameters include a threshold voltage (Vth) of 2.49 V and a rectification ratio (RR) of 10^4.
  • Accurate ReLU operation was confirmed with a synaptic conductance threshold (Gtot) of 110 nS, and successful multilayer signal propagation was achieved.

Conclusions:

  • The PN heterojunction neuron offers a simple, scalable, and intrinsically nonlinear solution for neuron implementation.
  • It effectively addresses the area and complexity challenges of CMOS-based neuron circuits.
  • This device shows significant potential for highly integrated and energy-efficient HDNNs.