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Understanding Memory01:19

Understanding Memory

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Memory is the retention of information or experiences over time, facilitated through three main processes: encoding, storage, and retrieval. Encoding is the process of inputting information into the memory system. For instance, when listening to a lecture, watching a play, reading a book, or having a conversation, the brain is actively encoding information. This initial stage involves transforming sensory input into a form that can be processed and stored by the brain. Various factors, such as...
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System of Memory01:23

System of Memory

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Memory is categorized into three major systems: sensory memory, short-term memory (STM), and long-term memory (LTM). These systems differ in their capacity and the duration for which they can hold information. Sensory memory captures raw sensory input from the environment, holding it for just a few seconds or less. For example, on hearing a brief, loud sound, like a car horn honking, the sound seems to linger in the mind for a moment even after it stops. This is an instance of sensory memory...
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Higher Mental Functions of Brain: Learning and Memory01:26

Higher Mental Functions of Brain: Learning and Memory

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Memory is one of the most vital higher mental functions of the brain. Memory is closely related to learning because it enables us to retain information and experiences from our past to use them in our present life. It also helps us to remember facts, events, and skills, such as riding a bike or swimming. There are two types of memory — declarative memory, which involves memorizing facts or events, and procedural memory, which enables us to remember how to do something like writing or...
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Long-Term Memory01:18

Long-Term Memory

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Long-term memory is a relatively permanent type of memory, capable of storing vast amounts of information over extended periods. Its storage capacity is generally considered unlimited.
Long-term memory can be categorized into two primary types: explicit and implicit memory. Explicit memory, also known as declarative memory, involves the conscious recollection of information that we deliberately try to remember, recall, and articulate. This type of memory encompasses specific facts, events, and...
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MOS Capacitor01:25

MOS Capacitor

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A Metal-Oxide-Semiconductor (MOS) capacitor is a fundamental structure used extensively in semiconductor device technology, particularly in the fabrication of integrated circuits and MOSFETs (metal-oxide-semiconductor field-effect transistors). The MOS capacitor consists of three layers: a metal gate, a dielectric oxide, and a semiconductor substrate.
The metal gate is typically made from highly conductive materials such as aluminum or polysilicon. Beneath the metal gate lies a thin layer of...
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Ferromagnetism01:31

Ferromagnetism

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Materials like iron, nickel, and cobalt consist of magnetic domains, within which the magnetic dipoles are arranged parallel to each other. The magnetic dipoles are rigidly aligned in the same direction within a domain by quantum mechanical coupling among the atoms. This coupling is so strong that even thermal agitation at room temperature cannot break it. The result is that each domain has a net dipole moment. However, some materials have weaker coupling, and are ferromagnetic at lower...
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Related Experiment Video

Updated: Jan 13, 2026

Assembly and Characterization of Biomolecular Memristors Consisting of Ion Channel-doped Lipid Membranes
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Assembly and Characterization of Biomolecular Memristors Consisting of Ion Channel-doped Lipid Membranes

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A ferroelectric-memristor memory for both training and inference.

Michele Martemucci1,2, François Rummens2, Yannick Malot2

  • 1Université Grenoble Alpes, CEA-Leti, Grenoble, France.

Nature Electronics
|October 29, 2025
PubMed
Summary

Researchers developed a novel unified memory stack for edge artificial intelligence (AI). This hybrid memory enables efficient inference and learning, overcoming limitations of current technologies for AI hardware.

Keywords:
Electrical and electronic engineeringElectronic devices

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Area of Science:

  • Materials Science
  • Computer Engineering
  • Artificial Intelligence

Background:

  • Edge artificial intelligence (AI) demands energy-efficient inference and learning capabilities.
  • Existing memory technologies face limitations in endurance, programming energy, and read processes, hindering AI development.
  • A unified memory solution is needed to combine the strengths of different memory types for AI applications.

Purpose of the Study:

  • To develop a novel unified memory stack integrating memristor and ferroelectric capacitor functionalities.
  • To address the limitations of current memory technologies for energy-efficient edge AI.
  • To demonstrate an on-chip learning solution using the hybrid memory array.

Main Methods:

  • Fabrication of a unified memory stack using silicon-doped hafnium oxide and a titanium scavenging layer.
  • Integration into a complementary metal-oxide-semiconductor (CMOS) back end of line process.
  • Creation of an 18,432-device hybrid array with on-chip CMOS periphery circuits.

Main Results:

  • The unified memory stack successfully functions as both a memristor and a ferroelectric capacitor.
  • A hybrid array demonstrated efficient weight transfer between memory types without a digital-to-analogue converter.
  • The on-chip learning solution achieved competitive performance with software models on benchmarks.

Conclusions:

  • The developed unified memory stack offers a promising solution for energy-efficient edge AI.
  • This hybrid memory architecture overcomes key limitations of existing technologies for AI hardware.
  • The demonstrated on-chip learning validates the potential of this approach for future AI systems.