A Highly Linear Analog-to-Spike Converter

A Highly Linear Analog-to-Spike Converter | Research Square Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Help Center Sign In Submit a Preprint Cite Share Download PDF Case Report A Highly Linear Analog-to-Spike Converter Meysam Akbari, Erika Covi This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8938092/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract This brief presents a low-voltage differential voltage-to-spike encoder based on a bulk-driven transconductor and dual leaky integrate-and-fire (LIF) neurons. The proposed transconductor employs a bulk-driven differential input pair with diode-connected source degeneration to enhance linearity under reduced supply voltage operation. The circuit converts an input differential voltage into complementary output currents, which are directly injected into two LIF neurons to encode the input amplitude into spike firing rates. The two neurons generate opposite-polarity spike trains (0–VDD and 0–VSS), enabling fully differential spike encoding with improved dynamic range, common-mode rejection, and noise robustness compared to single-ended encoders. The design was implemented and simulated in a standard 0.18-μm CMOS technology, demonstrating low-voltage operation, enhanced linearity, and wide input dynamic range suitable for neuromorphic and mixed-signal sensor interface applications. Electrical Engineering Transconductor Signal-to-spike encoder Linearity Neuron Bulk driven Full Text Additional Declarations The authors declare no competing interests. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information

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