Related Experiment Video
Updated: May 16, 2025

Processing of Bulk Nanocrystalline Metals at the US Army Research Laboratory
Published on: March 7, 2018
Grain size engineering via a Hf0.5Zr0.5O2 seed layer for FeFET memory and synaptic devices
Junhyeok Park1, Chulwon Chung2, Boncheol Ku1
1Division of Materials Science and Engineering, Hanyang University, Korea. cchoi@hanyang.ac.kr.
This study enhances ferroelectric field-effect transistors (FeFETs) using novel electrode and deposition methods. These advancements significantly boost ferroelectric properties, memory performance, and synaptic computing accuracy for AI applications.
Area of Science:
- Materials Science
- Solid-State Physics
- Electrical Engineering
Background:
- Ferroelectric field-effect transistors (FeFETs) are crucial for non-volatile memory and neuromorphic computing.
- Optimizing ferroelectric properties and device performance remains a key challenge in FeFET development.
Purpose of the Study:
- To investigate the impact of replacement electrode solid phase epitaxy (SPE) and high-temperature atomic layer deposition (ALD) on FeFET characteristics.
- To enhance ferroelectric, memory, and synaptic functionalities of FeFETs through advanced material engineering.
Main Methods:
- Fabrication of top-gate FeFETs utilizing the SPE method for electrode engineering.
- Employing high deposition temperatures during ALD to control film microstructure.
- Characterization of ferroelectric properties, memory window, endurance, and synaptic behavior.
Main Results:
- Reduced grain size and suppressed monoclinic phase formation in the ferroelectric layer.
- Increased remanent polarization (35%) and coercive field (50%).
- Enhanced memory window (0.3 V to 0.9 V), three orders of magnitude improvement in endurance, increased conductance states (100 to 136), and a higher Gmax/Gmin ratio (5.16 to 90).
- Improved weight update linearity and a 20% increase in inference accuracy (65% to 85%) on the MNIST dataset.
Conclusions:
- The combined SPE and high-temperature ALD approach effectively optimizes FeFET performance.
- Engineered FeFETs show significant potential for advanced memory and efficient artificial intelligence hardware acceleration.
More Related Videos
Related Concept Videos
Design Example: Aggregate Gradation
The grading, or particle-size distribution, of sand is determined using sieve analysis, with standard sizes ranging from 150 μm to 10 mm (ASTM No. 100 sieve to 3⁄8 in. sieve). Sand is...
Sieve Analysis and Grading Curves
Types of Aggregate Grading
Well-graded aggregates include a complete range of necessary size fractions that fit together to create a dense matrix with minimal voids, represented by a smooth, continuous gradation curve. This type of grading ensures good...
Maximum Size of Aggregate
Pore Size Distribution
Adequate...
Fineness Modulus
Consider performing sieve analysis on sand through a set of ASTM sieves. The weight of aggregate retained in each sieve and pan placed at the bottom is recorded, as given in Column B of Table 1.
To determine the fineness modulus of...

