Demonstration of an UltraLow Energy PD-SOI FinFET Based LIF Neuron for SNN

In this article, partially depleted silicon on insulator (PD-SOI) FinFET based LIF neuron is demonstrated to mimic biological neuronal behaviour with aid of well-calibrated 3D TCAD simulation. The floating body effect of PD-SOI FinFET is used to store the holes generated by the impact ionisation (&#...

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Veröffentlicht in:IEEE transactions on nanotechnology 2022, Vol.21, p.434-441
Hauptverfasser: Rajakumari, V, Pradhan, K P
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description In this article, partially depleted silicon on insulator (PD-SOI) FinFET based LIF neuron is demonstrated to mimic biological neuronal behaviour with aid of well-calibrated 3D TCAD simulation. The floating body effect of PD-SOI FinFET is used to store the holes generated by the impact ionisation ('II'), which exhibits the integration phenomenon and recombination manifests the leaky function. It shows 5.4 fJ of energy per spike, which is significantly lower than other FinFET based neurons reported till date. Moreover, it needs only 1.8 V of supply voltage, which is lower as compared to its equivalent bulk FinFET and PD-SOI MOSFET based LIF neurons. The proposed PD-SOI FinFET LIF neuron shows megahertz range of spiking frequency, that is \sim 10 ^{5}× higher than biological neuron (\sim1-10 Hz). Furthermore, the effective area of the FinFET is optimized as 0.023 \mum ^{2}. Thus, the proposed PD-SOI FinFET based LIF neuron is more attractive for large scale hardware implementation of SNN, due to its energy and area efficiency comparable with biological neuron along with CMOS compatibility.
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The floating body effect of PD-SOI FinFET is used to store the holes generated by the impact ionisation ('II'), which exhibits the integration phenomenon and recombination manifests the leaky function. It shows 5.4 fJ of energy per spike, which is significantly lower than other FinFET based neurons reported till date. Moreover, it needs only 1.8 V of supply voltage, which is lower as compared to its equivalent bulk FinFET and PD-SOI MOSFET based LIF neurons. The proposed PD-SOI FinFET LIF neuron shows megahertz range of spiking frequency, that is <inline-formula><tex-math notation="LaTeX">\sim 10 ^{5}</tex-math></inline-formula>× higher than biological neuron (<inline-formula><tex-math notation="LaTeX">\sim</tex-math></inline-formula>1-10 Hz). Furthermore, the effective area of the FinFET is optimized as 0.023 <inline-formula><tex-math notation="LaTeX">\mu</tex-math></inline-formula>m<inline-formula><tex-math notation="LaTeX"> ^{2}</tex-math></inline-formula>. Thus, the proposed PD-SOI FinFET based LIF neuron is more attractive for large scale hardware implementation of SNN, due to its energy and area efficiency comparable with biological neuron along with CMOS compatibility.]]></description><identifier>ISSN: 1536-125X</identifier><identifier>EISSN: 1941-0085</identifier><identifier>DOI: 10.1109/TNANO.2022.3195698</identifier><identifier>CODEN: ITNECU</identifier><language>eng</language><publisher>New York: IEEE</publisher><subject>Biological system modeling ; Biology ; Computational modeling ; FinFETs ; Floating bodies ; Hardware ; Impact ionisation ; leaky-integrate and fire ; Logic gates ; MOSFETs ; Neurons ; PD-SOI FinFET ; SOI (semiconductors) ; spiking neural network</subject><ispartof>IEEE transactions on nanotechnology, 2022, Vol.21, p.434-441</ispartof><rights>Copyright The Institute of Electrical and Electronics Engineers, Inc. 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The floating body effect of PD-SOI FinFET is used to store the holes generated by the impact ionisation ('II'), which exhibits the integration phenomenon and recombination manifests the leaky function. It shows 5.4 fJ of energy per spike, which is significantly lower than other FinFET based neurons reported till date. Moreover, it needs only 1.8 V of supply voltage, which is lower as compared to its equivalent bulk FinFET and PD-SOI MOSFET based LIF neurons. The proposed PD-SOI FinFET LIF neuron shows megahertz range of spiking frequency, that is <inline-formula><tex-math notation="LaTeX">\sim 10 ^{5}</tex-math></inline-formula>× higher than biological neuron (<inline-formula><tex-math notation="LaTeX">\sim</tex-math></inline-formula>1-10 Hz). Furthermore, the effective area of the FinFET is optimized as 0.023 <inline-formula><tex-math notation="LaTeX">\mu</tex-math></inline-formula>m<inline-formula><tex-math notation="LaTeX"> ^{2}</tex-math></inline-formula>. Thus, the proposed PD-SOI FinFET based LIF neuron is more attractive for large scale hardware implementation of SNN, due to its energy and area efficiency comparable with biological neuron along with CMOS compatibility.]]></description><subject>Biological system modeling</subject><subject>Biology</subject><subject>Computational modeling</subject><subject>FinFETs</subject><subject>Floating bodies</subject><subject>Hardware</subject><subject>Impact ionisation</subject><subject>leaky-integrate and fire</subject><subject>Logic gates</subject><subject>MOSFETs</subject><subject>Neurons</subject><subject>PD-SOI FinFET</subject><subject>SOI (semiconductors)</subject><subject>spiking neural network</subject><issn>1536-125X</issn><issn>1941-0085</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2022</creationdate><recordtype>article</recordtype><sourceid>RIE</sourceid><recordid>eNo9kF1PwjAUhhejiYj-Ab1p4vXw9GtrLxFBMcswARLvmrK1ZgRWbEcM_94ixKtz3uR9zkmeJLnHMMAY5NOiHJazAQFCBhRLnklxkfSwZDgFEPwy7pxmKSb88zq5CWENgPOMi17y_mK2rg2d113jWuQs0i1abmIu3A8at8Z_HdDHSzqfTdGkaSfjBXrWwdSomE5QafY-QtZ5NC_L2-TK6k0wd-fZT5axPnpLi9nrdDQs0opI3qUYKK0qa6TOLWeG5hw0IwIkMEuFzlZaUsEZXzFbM6gMFjbGelUTSS2nlvaTx9PdnXffexM6tXZ738aXiuRAQUogWWyRU6vyLgRvrNr5Zqv9QWFQR2fqz5k6OlNnZxF6OEGNMeYfkILlDAT9Bbq1ZVw</recordid><startdate>2022</startdate><enddate>2022</enddate><creator>Rajakumari, V</creator><creator>Pradhan, K P</creator><general>IEEE</general><general>The Institute of Electrical and Electronics Engineers, Inc. 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The floating body effect of PD-SOI FinFET is used to store the holes generated by the impact ionisation ('II'), which exhibits the integration phenomenon and recombination manifests the leaky function. It shows 5.4 fJ of energy per spike, which is significantly lower than other FinFET based neurons reported till date. Moreover, it needs only 1.8 V of supply voltage, which is lower as compared to its equivalent bulk FinFET and PD-SOI MOSFET based LIF neurons. The proposed PD-SOI FinFET LIF neuron shows megahertz range of spiking frequency, that is <inline-formula><tex-math notation="LaTeX">\sim 10 ^{5}</tex-math></inline-formula>× higher than biological neuron (<inline-formula><tex-math notation="LaTeX">\sim</tex-math></inline-formula>1-10 Hz). Furthermore, the effective area of the FinFET is optimized as 0.023 <inline-formula><tex-math notation="LaTeX">\mu</tex-math></inline-formula>m<inline-formula><tex-math notation="LaTeX"> ^{2}</tex-math></inline-formula>. Thus, the proposed PD-SOI FinFET based LIF neuron is more attractive for large scale hardware implementation of SNN, due to its energy and area efficiency comparable with biological neuron along with CMOS compatibility.]]></abstract><cop>New York</cop><pub>IEEE</pub><doi>10.1109/TNANO.2022.3195698</doi><tpages>8</tpages><orcidid>https://orcid.org/0000-0002-8107-6247</orcidid><orcidid>https://orcid.org/0000-0002-7313-294X</orcidid></addata></record>
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subjects Biological system modeling
Biology
Computational modeling
FinFETs
Floating bodies
Hardware
Impact ionisation
leaky-integrate and fire
Logic gates
MOSFETs
Neurons
PD-SOI FinFET
SOI (semiconductors)
spiking neural network
title Demonstration of an UltraLow Energy PD-SOI FinFET Based LIF Neuron for SNN
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