{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,8,24]],"date-time":"2025-08-24T01:28:35Z","timestamp":1755998915533,"version":"3.37.3"},"reference-count":33,"publisher":"IOP Publishing","issue":"1","license":[{"start":{"date-parts":[[2021,12,28]],"date-time":"2021-12-28T00:00:00Z","timestamp":1640649600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"},{"start":{"date-parts":[[2021,12,28]],"date-time":"2021-12-28T00:00:00Z","timestamp":1640649600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/iopscience.iop.org\/info\/page\/text-and-data-mining"}],"funder":[{"DOI":"10.13039\/501100002183","name":"Department of Electronics and Information Technology, Ministry of Communications and Information Technology","doi-asserted-by":"crossref","id":[{"id":"10.13039\/501100002183","id-type":"DOI","asserted-by":"crossref"}]},{"DOI":"10.13039\/501100005808","name":"Indian Institute of Technology Bombay","doi-asserted-by":"crossref","id":[{"id":"10.13039\/501100005808","id-type":"DOI","asserted-by":"crossref"}]},{"DOI":"10.13039\/501100001409","name":"Department of Science and Technology, Ministry of Science and Technology, India","doi-asserted-by":"crossref","id":[{"id":"10.13039\/501100001409","id-type":"DOI","asserted-by":"crossref"}]},{"name":"Prime Minister Research Fellowship"},{"DOI":"10.13039\/100000028","name":"Semiconductor Research Corporation","doi-asserted-by":"crossref","id":[{"id":"10.13039\/100000028","id-type":"DOI","asserted-by":"crossref"}]}],"content-domain":{"domain":["iopscience.iop.org"],"crossmark-restriction":false},"short-container-title":["Neuromorph. Comput. Eng."],"published-print":{"date-parts":[[2022,3,1]]},"abstract":"<jats:title>Abstract<\/jats:title>\n               <jats:p>Emerging non-volatile memories have been proposed for a wide range of applications, from easing the von-Neumann bottleneck to neuromorphic applications. Specifically, scalable RRAMs based on Pr<jats:sub>1\u2212<jats:italic>x<\/jats:italic>\n                  <\/jats:sub>Ca<jats:sub>\n                     <jats:italic>x<\/jats:italic>\n                  <\/jats:sub>MnO<jats:sub>3<\/jats:sub> (PCMO) exhibit analog\u00a0switching have been demonstrated as an integrating neuron, an analog\u00a0synapse, and a voltage-controlled oscillator. More recently, the inherent stochasticity of memristors has been proposed for efficient hardware implementations of Boltzmann machines. However, as the problem size scales, the number of neurons increases and controlling the stochastic distribution tightly over many iterations is necessary. This requires parametric control over stochasticity. Here, we characterize the stochastic set in PCMO RRAMs. We identify that the set time distribution depends on the internal state of the device (i.e., resistance) in addition to external input (i.e., voltage pulse). This requires the confluence of contradictory properties like stochastic switching as well as deterministic state control in the same device. Unlike \u2018stochastic-everywhere\u2019 filamentary memristors, in PCMO RRAMs, we leverage the (i) stochastic set in negative polarity and (ii) deterministic analog\u00a0Reset in positive polarity to demonstrate 100\u00d7 reduced set time distribution drift. The impact on Boltzmann machines\u2019 performance is analyzed and as opposed to the \u2018fixed external input stochasticity\u2019, the \u2018state-monitored stochasticity\u2019 can solve problems 20\u00d7 larger in size. State monitoring also tunes out the device-to-device variability effect on distributions providing 10\u00d7 better performance. In addition to the physical insights, this study establishes the use of experimental stochasticity in PCMO RRAMs in stochastic recurrent neural networks reliably over many iterations.<\/jats:p>","DOI":"10.1088\/2634-4386\/ac408a","type":"journal-article","created":{"date-parts":[[2021,12,7]],"date-time":"2021-12-07T14:37:22Z","timestamp":1638887842000},"page":"014001","update-policy":"https:\/\/doi.org\/10.1088\/crossmark-policy","source":"Crossref","is-referenced-by-count":5,"title":["Stochasticity invariance control in Pr<sub>1\u2212x\n               <\/sub>Ca<sub>\n                  x\n               <\/sub>MnO<sub>3<\/sub> RRAM to enable large-scale stochastic recurrent neural networks"],"prefix":"10.1088","volume":"2","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-9191-1632","authenticated-orcid":false,"given":"Vivek","family":"Saraswat","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1498-5993","authenticated-orcid":false,"given":"Udayan","family":"Ganguly","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"266","published-online":{"date-parts":[[2021,12,28]]},"reference":[{"key":"nceac408abib1","doi-asserted-by":"publisher","first-page":"529","DOI":"10.1038\/s41565-020-0655-z","article-title":"Memory devices and applications for in-memory computing","volume":"15","author":"Sebastian","year":"2020","journal-title":"Nat. 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Published by IOP Publishing Ltd","name":"copyright_information","label":"Copyright Information"},{"value":"2021-10-07","name":"date_received","label":"Date Received","group":{"name":"publication_dates","label":"Publication dates"}},{"value":"2021-12-06","name":"date_accepted","label":"Date Accepted","group":{"name":"publication_dates","label":"Publication dates"}},{"value":"2021-12-28","name":"date_epub","label":"Online publication date","group":{"name":"publication_dates","label":"Publication dates"}}]}}