A Self‐Organizing Map Spiking Neural Network Based on Tin Oxide Memristive Synapses and Neurons
Abstract Neuromorphic computing systems are promising alternatives in areas such as pattern recognition and image processing. This work focuses on the fabrication of tin oxide memristors (Ag/SnO2/Pt) to emulate artificial synapses and neurons. These tin oxide memristors demonstrate stable switching...
Saved in:
| Main Authors: | , , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Wiley-VCH
2025-02-01
|
| Series: | Advanced Electronic Materials |
| Subjects: | |
| Online Access: | https://doi.org/10.1002/aelm.202400421 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1849327579641675776 |
|---|---|
| author | Yu Wang Yanzhong Zhang Yanji Wang Xinpeng Wang Hao Zhang Rongqing Xu Yi Tong |
| author_facet | Yu Wang Yanzhong Zhang Yanji Wang Xinpeng Wang Hao Zhang Rongqing Xu Yi Tong |
| author_sort | Yu Wang |
| collection | DOAJ |
| description | Abstract Neuromorphic computing systems are promising alternatives in areas such as pattern recognition and image processing. This work focuses on the fabrication of tin oxide memristors (Ag/SnO2/Pt) to emulate artificial synapses and neurons. These tin oxide memristors demonstrate stable switching between threshold switch (TS) and resistive switch (RS) modes, achieved by adjusting the compliance current. Notably, this memristor achieves extremely low switching voltage and excellent cycle endurance. Moreover, the conductance value of the memristor can continuously transform under different illumination conditions, such as white light and purple light. A single tin oxide memristor device is used to model typical neuromorphic responses, such as synaptic plasticity and artificial neuron impulse responses. This approach offers a promising solution for high‐density, low‐power, brain‐inspired computing chips. Additionally, memristive Leaky Integrate‐and‐Fire (LIF) neuron and synapse models are designed and integrated for the first time into a Self‐Organizing Map Spiking Neural Network (SOM‐SNN) architecture. Applying this architecture to an unsupervised learning self‐organizing map memristor SNN achieved an impressive 94% recognition rate on the MNIST dataset. This study elucidates the potential for seamlessly integrating memristors into neuromorphic systems. |
| format | Article |
| id | doaj-art-bcefa3d0fe154500a960620941b319dc |
| institution | Kabale University |
| issn | 2199-160X |
| language | English |
| publishDate | 2025-02-01 |
| publisher | Wiley-VCH |
| record_format | Article |
| series | Advanced Electronic Materials |
| spelling | doaj-art-bcefa3d0fe154500a960620941b319dc2025-08-20T03:47:49ZengWiley-VCHAdvanced Electronic Materials2199-160X2025-02-01112n/an/a10.1002/aelm.202400421A Self‐Organizing Map Spiking Neural Network Based on Tin Oxide Memristive Synapses and NeuronsYu Wang0Yanzhong Zhang1Yanji Wang2Xinpeng Wang3Hao Zhang4Rongqing Xu5Yi Tong6Engineering & College of Flexible Electronics (Future Technology) Nanjing University of Posts and Telecommunications Nanjing 210023 ChinaCollege of Integrated Circuit Science and Engineering (Industry‐Education Integration School) Nanjing University of Posts and Telecommunications Nanjing 210023 ChinaCollege of Integrated Circuit Science and Engineering (Industry‐Education Integration School) Nanjing University of Posts and Telecommunications Nanjing 210023 ChinaGusu Lab Suzhou 215000 ChinaGusu Lab Suzhou 215000 ChinaEngineering & College of Flexible Electronics (Future Technology) Nanjing University of Posts and Telecommunications Nanjing 210023 ChinaGusu Lab Suzhou 215000 ChinaAbstract Neuromorphic computing systems are promising alternatives in areas such as pattern recognition and image processing. This work focuses on the fabrication of tin oxide memristors (Ag/SnO2/Pt) to emulate artificial synapses and neurons. These tin oxide memristors demonstrate stable switching between threshold switch (TS) and resistive switch (RS) modes, achieved by adjusting the compliance current. Notably, this memristor achieves extremely low switching voltage and excellent cycle endurance. Moreover, the conductance value of the memristor can continuously transform under different illumination conditions, such as white light and purple light. A single tin oxide memristor device is used to model typical neuromorphic responses, such as synaptic plasticity and artificial neuron impulse responses. This approach offers a promising solution for high‐density, low‐power, brain‐inspired computing chips. Additionally, memristive Leaky Integrate‐and‐Fire (LIF) neuron and synapse models are designed and integrated for the first time into a Self‐Organizing Map Spiking Neural Network (SOM‐SNN) architecture. Applying this architecture to an unsupervised learning self‐organizing map memristor SNN achieved an impressive 94% recognition rate on the MNIST dataset. This study elucidates the potential for seamlessly integrating memristors into neuromorphic systems.https://doi.org/10.1002/aelm.202400421SNNSnO2artificial neuronsartificial synapsememristor |
| spellingShingle | Yu Wang Yanzhong Zhang Yanji Wang Xinpeng Wang Hao Zhang Rongqing Xu Yi Tong A Self‐Organizing Map Spiking Neural Network Based on Tin Oxide Memristive Synapses and Neurons Advanced Electronic Materials SNN SnO2 artificial neurons artificial synapse memristor |
| title | A Self‐Organizing Map Spiking Neural Network Based on Tin Oxide Memristive Synapses and Neurons |
| title_full | A Self‐Organizing Map Spiking Neural Network Based on Tin Oxide Memristive Synapses and Neurons |
| title_fullStr | A Self‐Organizing Map Spiking Neural Network Based on Tin Oxide Memristive Synapses and Neurons |
| title_full_unstemmed | A Self‐Organizing Map Spiking Neural Network Based on Tin Oxide Memristive Synapses and Neurons |
| title_short | A Self‐Organizing Map Spiking Neural Network Based on Tin Oxide Memristive Synapses and Neurons |
| title_sort | self organizing map spiking neural network based on tin oxide memristive synapses and neurons |
| topic | SNN SnO2 artificial neurons artificial synapse memristor |
| url | https://doi.org/10.1002/aelm.202400421 |
| work_keys_str_mv | AT yuwang aselforganizingmapspikingneuralnetworkbasedontinoxidememristivesynapsesandneurons AT yanzhongzhang aselforganizingmapspikingneuralnetworkbasedontinoxidememristivesynapsesandneurons AT yanjiwang aselforganizingmapspikingneuralnetworkbasedontinoxidememristivesynapsesandneurons AT xinpengwang aselforganizingmapspikingneuralnetworkbasedontinoxidememristivesynapsesandneurons AT haozhang aselforganizingmapspikingneuralnetworkbasedontinoxidememristivesynapsesandneurons AT rongqingxu aselforganizingmapspikingneuralnetworkbasedontinoxidememristivesynapsesandneurons AT yitong aselforganizingmapspikingneuralnetworkbasedontinoxidememristivesynapsesandneurons AT yuwang selforganizingmapspikingneuralnetworkbasedontinoxidememristivesynapsesandneurons AT yanzhongzhang selforganizingmapspikingneuralnetworkbasedontinoxidememristivesynapsesandneurons AT yanjiwang selforganizingmapspikingneuralnetworkbasedontinoxidememristivesynapsesandneurons AT xinpengwang selforganizingmapspikingneuralnetworkbasedontinoxidememristivesynapsesandneurons AT haozhang selforganizingmapspikingneuralnetworkbasedontinoxidememristivesynapsesandneurons AT rongqingxu selforganizingmapspikingneuralnetworkbasedontinoxidememristivesynapsesandneurons AT yitong selforganizingmapspikingneuralnetworkbasedontinoxidememristivesynapsesandneurons |