|Publisher DOI:||10.1109/JSSC.2018.2828823||Title:||A 22 V Compliant 56 μ W twin-track active charge balancing enabling 100% charge compensation even in monophasic and 36% amplitude correction in biphasic neural stimulators||Language:||English||Authors:||Butz, Natalie
|Issue Date:||Aug-2018||Source:||IEEE Journal of Solid-State Circuits 8 (53): 2298-2310 (2018-08)||Journal or Series Name:||IEEE Journal of Solid-State Circuits||Abstract (english):||Closed-loop charge balancers make a considerable contribution to a safe and secure electrical stimulation and additionally present an alternative to bulky blocking capacitors. This CMOS-integrated, 22 V compliant Twin-Track active charge balancer accomplishes both instantaneous and long-term balanced conditions. Long-term charge balancing is achieved by a cause-based PI-controlled offset compensation of the remaining electrode voltage. A Gm of only 1.5 nS offers a large (8 ms) time constant with only a 12 pF capacitor and leads to the first integrated PI-controller for offset compensation in neural implants. Instantaneous autonomous balancing is realized by a second concept, the consequence-based Inter-Pulse Charge Control. It uses a class-B architecture to supply the compensation currents. Thus, no additional references are required to define the safety window of the remaining electrode voltage. It consumes 31.8 μW in the idle state, while the delivered output power can be as high as 11 mW. The two complementary approaches can be combined to one system, providing the opportunity to achieve a better performance. The system consumes 56 μW and features a 36% mismatch correction in biphasic stimuli amplitude, as well as an autonomous 100% charge removal at maximum compensation amplitudes of 500 μA , which makes it a suitable complement to monophasic stimulators.||URI:||http://hdl.handle.net/11420/2983||ISSN:||0018-9200||Institute:||Integrierte Schaltungen E-9||Type:||(wissenschaftlicher) Artikel||Funded by:||This work was supported in part by the German Research Foundation (DFG) under Grant MA 2193/20-1 and in part by the BrainLinks-BrainTools Cluster of Excellence (DFG) under Grant EXC 1086.|
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