{"675450":{"#nid":"675450","#data":{"type":"event","title":"Ph.D. Dissertation Defense - Arindam Mandal","body":[{"value":"\u003Cp\u003E\u003Cstrong\u003ETitle\u003C\/strong\u003E\u003Cem\u003E:\u0026nbsp; Design of Digitally-Assisted and Artifact-Robust Next-Generation Bi-directional Neural Interfaces\u003C\/em\u003E\u003C\/p\u003E\u003Cp\u003E\u003Cstrong\u003ECommittee:\u003C\/strong\u003E\u003C\/p\u003E\u003Cp\u003EDr.\u0026nbsp;Visvesh Sathe, ECE, Chair, Advisor\u003C\/p\u003E\u003Cp\u003EDr.\u0026nbsp;Shaolan Li, ECE\u003C\/p\u003E\u003Cp\u003EDr.\u0026nbsp;Muhannad Bakir, ECE\u003C\/p\u003E\u003Cp\u003EDr.\u0026nbsp;Farrokh Ayazi, ECE\u003C\/p\u003E\u003Cp\u003EDr.\u0026nbsp;Sam Sober, Emory\u003C\/p\u003E","summary":"","format":"limited_html"}],"field_subtitle":"","field_summary":[{"value":"\u003Cp\u003ENext generation adaptive neuromodulation systems require miniature, high-density, artifact-tolerant neural sensing, low loop-latency, and spatially selective, programmable neural stimulation. We introduce a novel digitally-assisted and artifact-robust neural stimulator and recording front-end to meet these needs of future bidirectional neuromodulation.\u003C\/p\u003E\u003Cp\u003EThe proposed neural demonstrates spatially targeted stimulation with suppression of driver nonideality induced common-mode (CM) artifacts in low-latency closed-loop neuromodulation. The proposed approach utilizes computationally guided concurrent stimulation across multiple electrodes to achieve spatial selectivity in stimulation. The stimulator architecture supports flexible \u0026nbsp;storage of multiple pre-computed vector stimulus patterns in integrated memory, allowing rapid access and delivery of selected patterns in response to decoded neural activity. Additionally, a combination of the stimulator circuit architecture and mixed-signal current imbalance compensation techniques effectively suppress CM artifacts to below 50 mV. These techniques are demonstrated in a 180 nm HV CMOS test-chip containing 46 stimulation drivers of 26 V compliance and validated through a combination of bench, saline and in vivo tests.\u003C\/p\u003E\u003Cp\u003EOur proposed 32-channel recording analog front-end (AFE) architecture exhibits rapid recovery from differential-mode large stimulation artifacts while delivering high-resolution digitized data with ultra-low latency. The time-multiplexed AFE architecture ensures low area and power consumption, paving the way for building high-density neural interfaces. We introduce a novel technique for the correction of feedback Digital-to-Analog Converter (DAC) non-linearities, contributing to enhanced Signal-to-Noise-and-Distortion Ratio (SNDR) performance. Additionally, the recording interface reduces the current draw from the input channels to prevent signal quality degradation. Fabricated in a 65 nm CMOS process, the direct digitization AFE achieves 85.4 dB SNDR in a 500 Hz bandwidth, resulting in a Schreier figure of merit of 172.1 dB, which is the highest among the existing time-multiplexed neural AFEs.\u003C\/p\u003E","format":"limited_html"}],"field_summary_sentence":[{"value":"Design of Digitally-Assisted and Artifact-Robust Next-Generation Bi-directional Neural Interfaces "}],"uid":"28475","created_gmt":"2024-07-13 21:28:35","changed_gmt":"2024-07-13 21:29:57","author":"Daniela Staiculescu","boilerplate_text":"","field_publication":"","field_article_url":"","field_event_time":{"event_time_start":"2024-07-24T10:00:00-04:00","event_time_end":"2024-07-24T12:00:00-04:00","event_time_end_last":"2024-07-24T12:00:00-04:00","gmt_time_start":"2024-07-24 14:00:00","gmt_time_end":"2024-07-24 16:00:00","gmt_time_end_last":"2024-07-24 16:00:00","rrule":null,"timezone":"America\/New_York"},"location":"Room W218, Van Leer","extras":[],"related_links":[{"url":"https:\/\/teams.microsoft.com\/l\/meetup-join\/19%3ameeting_MjA0OWIzNDctOWRjMi00NDExLTliMWYtMTEwY2RjZTk5Zjdl%40thread.v2\/0?context=%7b%22Tid%22%3a%22482198bb-ae7b-4b25-8b7a-6d7f32faa083%22%2c%22Oid%22%3a%221081cd96-aebe-44d3-bce8-38e9cb761417%22%7d","title":"Microsoft Teams Meeting link"}],"groups":[{"id":"434381","name":"ECE Ph.D. Dissertation Defenses"}],"categories":[],"keywords":[{"id":"100811","name":"Phd Defense"},{"id":"1808","name":"graduate students"}],"core_research_areas":[],"news_room_topics":[],"event_categories":[{"id":"1788","name":"Other\/Miscellaneous"}],"invited_audience":[{"id":"78771","name":"Public"}],"affiliations":[],"classification":[],"areas_of_expertise":[],"news_and_recent_appearances":[],"phone":[],"contact":[],"email":[],"slides":[],"orientation":[],"userdata":""}}}