Baibhab Chatterjee, K. G. Kumar, Shulan Xiao, Gourab Barik, K. Jayant, Shreyas Sen
{"title":"基于13.2pJ/Sample的时域双相准静态脑通信和直接模拟时间转换的无adc神经植入芯片(1.8\\mu\\ mathm {W}\\ 5.5$ mm3","authors":"Baibhab Chatterjee, K. G. Kumar, Shulan Xiao, Gourab Barik, K. Jayant, Shreyas Sen","doi":"10.1109/ESSCIRC55480.2022.9911420","DOIUrl":null,"url":null,"abstract":"Untethered miniaturized wireless neural sensor nodes with data transmission and energy harvesting capabilities call for circuit and system-level innovations to enable ultra-low energy deep implants for brain-machine interfaces. Realizing that the energy and size constraints of a neural implant motivate highly asymmetric system design (a small, low-power sensor and transmitter at the implant, with a relatively higher power receiver at a body-worn hub), we present Time-Domain Bi-Phasic Quasi-static Brain Communication (TD-BPQBC), offloading the burden of analog to digital conversion (ADC) and digital signal processing (DSP) to the receiver. The input analog signal is converted to time-domain pulse-width modulated (PWM) waveforms, and transmitted using the recently developed BPQBC method for reducing communication power in implants. The overall SoC consumes only $1.8 \\mu\\mathrm{W}$ power while sensing and communicating at 800kSps. The transmitter energy efficiency is only 1.1pJ/b, which is >30X better than the state-of-the-art, enabling a fully-electrical, energy-harvested, and connected in-brain sensor/stimulator node.","PeriodicalId":168466,"journal":{"name":"ESSCIRC 2022- IEEE 48th European Solid State Circuits Conference (ESSCIRC)","volume":"62 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2022-09-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"2","resultStr":"{\"title\":\"A $1.8\\\\mu\\\\mathrm{W}\\\\ 5.5$ mm3 ADC-less Neural Implant SoC utilizing 13.2pJ/Sample Time-domain Bi-phasic Quasi-static Brain Communication with Direct Analog to Time Conversion\",\"authors\":\"Baibhab Chatterjee, K. G. Kumar, Shulan Xiao, Gourab Barik, K. Jayant, Shreyas Sen\",\"doi\":\"10.1109/ESSCIRC55480.2022.9911420\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Untethered miniaturized wireless neural sensor nodes with data transmission and energy harvesting capabilities call for circuit and system-level innovations to enable ultra-low energy deep implants for brain-machine interfaces. Realizing that the energy and size constraints of a neural implant motivate highly asymmetric system design (a small, low-power sensor and transmitter at the implant, with a relatively higher power receiver at a body-worn hub), we present Time-Domain Bi-Phasic Quasi-static Brain Communication (TD-BPQBC), offloading the burden of analog to digital conversion (ADC) and digital signal processing (DSP) to the receiver. The input analog signal is converted to time-domain pulse-width modulated (PWM) waveforms, and transmitted using the recently developed BPQBC method for reducing communication power in implants. The overall SoC consumes only $1.8 \\\\mu\\\\mathrm{W}$ power while sensing and communicating at 800kSps. The transmitter energy efficiency is only 1.1pJ/b, which is >30X better than the state-of-the-art, enabling a fully-electrical, energy-harvested, and connected in-brain sensor/stimulator node.\",\"PeriodicalId\":168466,\"journal\":{\"name\":\"ESSCIRC 2022- IEEE 48th European Solid State Circuits Conference (ESSCIRC)\",\"volume\":\"62 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2022-09-19\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"2\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"ESSCIRC 2022- IEEE 48th European Solid State Circuits Conference (ESSCIRC)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ESSCIRC55480.2022.9911420\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"ESSCIRC 2022- IEEE 48th European Solid State Circuits Conference (ESSCIRC)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ESSCIRC55480.2022.9911420","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
A $1.8\mu\mathrm{W}\ 5.5$ mm3 ADC-less Neural Implant SoC utilizing 13.2pJ/Sample Time-domain Bi-phasic Quasi-static Brain Communication with Direct Analog to Time Conversion
Untethered miniaturized wireless neural sensor nodes with data transmission and energy harvesting capabilities call for circuit and system-level innovations to enable ultra-low energy deep implants for brain-machine interfaces. Realizing that the energy and size constraints of a neural implant motivate highly asymmetric system design (a small, low-power sensor and transmitter at the implant, with a relatively higher power receiver at a body-worn hub), we present Time-Domain Bi-Phasic Quasi-static Brain Communication (TD-BPQBC), offloading the burden of analog to digital conversion (ADC) and digital signal processing (DSP) to the receiver. The input analog signal is converted to time-domain pulse-width modulated (PWM) waveforms, and transmitted using the recently developed BPQBC method for reducing communication power in implants. The overall SoC consumes only $1.8 \mu\mathrm{W}$ power while sensing and communicating at 800kSps. The transmitter energy efficiency is only 1.1pJ/b, which is >30X better than the state-of-the-art, enabling a fully-electrical, energy-harvested, and connected in-brain sensor/stimulator node.