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However, the deployment of PRC is often limited by the quantization mismatch between continuous input signals and the finite dynamic range of hardware substrates. Conventional binary time\u2010multiplexing ensures high noise immunity but necessitates protracted pulse sequences that induce high latency and premature physical saturation. Conversely, multi\u2010valued analog encoding enhances information density but frequently induces state overlap, operating devoid of physical quantization boundaries. To resolve this physical\u2010information trade\u2010off, we propose a saturation\u2010constrained information capacity (SCIC) framework. This framework considers task\u2010specific encoding variables and substrate\u2010intrinsic constraints, enabling device\u2010informed optimization of input mapping. We experimentally demonstrate this framework on a highly uniform, selector\u2010free 32 \u00d7 32 crossbar array, utilizing the volatile dynamics of memristors. Under the experimentally selected encoding conditions, the SCIC\u2010guided RC achieves exceptional performance in temporal prediction (NRMSE of 0.019), speech recognition (free\u2010spoken digit dataset (FSDD) subset accuracy of \u223c94.5%), and real\u2010time obstacle avoidance. Our approach links encoding constraints to solid\u2010state device physics, providing a device\u2010informed method for PRC parameterization at the edge.<\/jats:p>","DOI":"10.1002\/adma.75096","type":"journal-article","created":{"date-parts":[[2026,9,21]],"date-time":"2026-09-21T06:24:42Z","timestamp":1789971882000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["A Memristor\u2010Based Saturation\u2010Constrained Information Capacity Framework for Temporal Information Processing"],"prefix":"10.1002","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-9476-6178","authenticated-orcid":false,"given":"Wenbin","family":"Guo","sequence":"first","affiliation":[{"name":"School of Integrated Circuits Anhui University  Hefei Anhui 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