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Neuromorphic crossbar circuit with nanoscale filamentary-switching binary memristors for speech recognition.

Truong SN, Ham SJ, Min KS - Nanoscale Res Lett (2014)

Bottom Line: The binary memristors which are based on filamentary-switching mechanism can be found more popularly and are easy to be fabricated than analog memristors that are rare in materials and need a more complicated fabrication process.From the statistical simulation, the recognition rate of the binary memristor crossbar is estimated to be degraded very little from 89.2% to 80%, though the percentage variation in memristance is increased very much from 0% to 15%.In contrast, the analog memristor crossbar loses its recognition rate significantly from 96% to 9% for the same percentage variation in memristance.

View Article: PubMed Central - HTML - PubMed

Affiliation: School of Electrical Engineering, Kookmin University, 77, Jeongneung-ro, Seongbuk-gu, Seoul 136-702, South Korea.

ABSTRACT
In this paper, a neuromorphic crossbar circuit with binary memristors is proposed for speech recognition. The binary memristors which are based on filamentary-switching mechanism can be found more popularly and are easy to be fabricated than analog memristors that are rare in materials and need a more complicated fabrication process. Thus, we develop a neuromorphic crossbar circuit using filamentary-switching binary memristors not using interface-switching analog memristors. The proposed binary memristor crossbar can recognize five vowels with 4-bit 64 input channels. The proposed crossbar is tested by 2,500 speech samples and verified to be able to recognize 89.2% of the tested samples. From the statistical simulation, the recognition rate of the binary memristor crossbar is estimated to be degraded very little from 89.2% to 80%, though the percentage variation in memristance is increased very much from 0% to 15%. In contrast, the analog memristor crossbar loses its recognition rate significantly from 96% to 9% for the same percentage variation in memristance.

No MeSH data available.


Analog memristors with interface-switching mechanism and binary memristors with filamentary-switching mechanism. (a) Analog memristor with the interface-switching mechanism [10,11], where the memristance value can be changed gradually from LRS to HRS, and (b) binary memristor with the filamentary-switching mechanism [12-17], where the memristance value can be changed very abruptly between LRS and HRS.
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Figure 2: Analog memristors with interface-switching mechanism and binary memristors with filamentary-switching mechanism. (a) Analog memristor with the interface-switching mechanism [10,11], where the memristance value can be changed gradually from LRS to HRS, and (b) binary memristor with the filamentary-switching mechanism [12-17], where the memristance value can be changed very abruptly between LRS and HRS.

Mentions: In realizing a memristor crossbar circuit, we can use either analog memristors [10,11] or binary memristors [12-17] as shown in Figure 2a,b. For the analog memristors in Figure 2a, their memristance value can be changed gradually and not abruptly due to the interface-switching mechanism. In the interface-switching behavior, the interface between the low-resistance region and the high-resistance region can be controlled precisely according to an applied voltage or current. As a result, we can store not only binary data but also analog data on the interface-switching memristors with high accuracy. However, materials that show the interface-switching behavior are not so popular, and the accuracy in controlling the memristance value is still considered to be a big concern. Also, even a small amount of memristance variation can degrade the overall accuracy severely in analog-memristor-based neuromorphic systems. On the contrary, most memristors are known that they are based on the filamentary-switching mechanism. In filamentary switching, memristors can have either a high resistance state (HRS) or a low resistance state (LRS) as represented in Figure 2b. By doing so, we can store only ‘1’ or ‘0’ on the filamentary-switching binary memristors.


Neuromorphic crossbar circuit with nanoscale filamentary-switching binary memristors for speech recognition.

Truong SN, Ham SJ, Min KS - Nanoscale Res Lett (2014)

Analog memristors with interface-switching mechanism and binary memristors with filamentary-switching mechanism. (a) Analog memristor with the interface-switching mechanism [10,11], where the memristance value can be changed gradually from LRS to HRS, and (b) binary memristor with the filamentary-switching mechanism [12-17], where the memristance value can be changed very abruptly between LRS and HRS.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4256962&req=5

Figure 2: Analog memristors with interface-switching mechanism and binary memristors with filamentary-switching mechanism. (a) Analog memristor with the interface-switching mechanism [10,11], where the memristance value can be changed gradually from LRS to HRS, and (b) binary memristor with the filamentary-switching mechanism [12-17], where the memristance value can be changed very abruptly between LRS and HRS.
Mentions: In realizing a memristor crossbar circuit, we can use either analog memristors [10,11] or binary memristors [12-17] as shown in Figure 2a,b. For the analog memristors in Figure 2a, their memristance value can be changed gradually and not abruptly due to the interface-switching mechanism. In the interface-switching behavior, the interface between the low-resistance region and the high-resistance region can be controlled precisely according to an applied voltage or current. As a result, we can store not only binary data but also analog data on the interface-switching memristors with high accuracy. However, materials that show the interface-switching behavior are not so popular, and the accuracy in controlling the memristance value is still considered to be a big concern. Also, even a small amount of memristance variation can degrade the overall accuracy severely in analog-memristor-based neuromorphic systems. On the contrary, most memristors are known that they are based on the filamentary-switching mechanism. In filamentary switching, memristors can have either a high resistance state (HRS) or a low resistance state (LRS) as represented in Figure 2b. By doing so, we can store only ‘1’ or ‘0’ on the filamentary-switching binary memristors.

Bottom Line: The binary memristors which are based on filamentary-switching mechanism can be found more popularly and are easy to be fabricated than analog memristors that are rare in materials and need a more complicated fabrication process.From the statistical simulation, the recognition rate of the binary memristor crossbar is estimated to be degraded very little from 89.2% to 80%, though the percentage variation in memristance is increased very much from 0% to 15%.In contrast, the analog memristor crossbar loses its recognition rate significantly from 96% to 9% for the same percentage variation in memristance.

View Article: PubMed Central - HTML - PubMed

Affiliation: School of Electrical Engineering, Kookmin University, 77, Jeongneung-ro, Seongbuk-gu, Seoul 136-702, South Korea.

ABSTRACT
In this paper, a neuromorphic crossbar circuit with binary memristors is proposed for speech recognition. The binary memristors which are based on filamentary-switching mechanism can be found more popularly and are easy to be fabricated than analog memristors that are rare in materials and need a more complicated fabrication process. Thus, we develop a neuromorphic crossbar circuit using filamentary-switching binary memristors not using interface-switching analog memristors. The proposed binary memristor crossbar can recognize five vowels with 4-bit 64 input channels. The proposed crossbar is tested by 2,500 speech samples and verified to be able to recognize 89.2% of the tested samples. From the statistical simulation, the recognition rate of the binary memristor crossbar is estimated to be degraded very little from 89.2% to 80%, though the percentage variation in memristance is increased very much from 0% to 15%. In contrast, the analog memristor crossbar loses its recognition rate significantly from 96% to 9% for the same percentage variation in memristance.

No MeSH data available.