<?xml version="1.0" encoding="UTF-8"?><rss version="2.0"
	xmlns:content="http://purl.org/rss/1.0/modules/content/"
	xmlns:wfw="http://wellformedweb.org/CommentAPI/"
	xmlns:dc="http://purl.org/dc/elements/1.1/"
	xmlns:atom="http://www.w3.org/2005/Atom"
	xmlns:sy="http://purl.org/rss/1.0/modules/syndication/"
	xmlns:slash="http://purl.org/rss/1.0/modules/slash/"
	>

<channel>
	<title>barrier height &#8211; BIOENGINEER.ORG</title>
	<atom:link href="https://bioengineer.org/tag/barrier-height/feed/" rel="self" type="application/rss+xml" />
	<link>https://bioengineer.org</link>
	<description>Bioengineering</description>
	<lastBuildDate>Fri, 23 Oct 2026 14:03:17 +0000</lastBuildDate>
	<language>en-US</language>
	<sy:updatePeriod>
	hourly	</sy:updatePeriod>
	<sy:updateFrequency>
	1	</sy:updateFrequency>
	<generator>https://wordpress.org/?v=7.1.3</generator>

<image>
	<url>https://bioengineer.org/wp-content/uploads/2019/09/cropped-bioengineering-32x32.png</url>
	<title>barrier height &#8211; BIOENGINEER.ORG</title>
	<link>https://bioengineer.org</link>
	<width>32</width>
	<height>32</height>
</image> 
<site xmlns="com-wordpress:feed-additions:1">72741379</site>	<item>
		<title>Feed-forward neural basis model delivers most accurate held-out temperature predictions for Schottky diode curves</title>
		<link>https://bioengineer.org/feed-forward-neural-basis-model-delivers-most-accurate-held-out-temperature-predictions-for-schottky-diode-curves/</link>
		
		<dc:creator><![CDATA[]]></dc:creator>
		<pubDate>Fri, 23 Oct 2026 14:03:17 +0000</pubDate>
				<category><![CDATA[Technology]]></category>
		<category><![CDATA[barrier height]]></category>
		<category><![CDATA[CdZnO]]></category>
		<category><![CDATA[current–voltage characteristics]]></category>
		<category><![CDATA[ideality factor]]></category>
		<category><![CDATA[interface-state density]]></category>
		<category><![CDATA[leave-one-temperature-out]]></category>
		<category><![CDATA[Machine Learning]]></category>
		<category><![CDATA[N-BEATS]]></category>
		<category><![CDATA[Neural Networks]]></category>
		<category><![CDATA[Schottky barrier diode]]></category>
		<category><![CDATA[thermionic emission]]></category>
		<category><![CDATA[zinc oxide]]></category>
		<guid isPermaLink="false">https://bioengineer.org/?p=449922</guid>

					<description><![CDATA[A leave-one-temperature-out comparison of ten models found that the feed-forward N-BEATS architecture gave the most accurate predictions of Schottky diode current–voltage behavior from 110 to 380 kelvin, though it was not statistically superior to the multi-layer perceptron.]]></description>
		
		
		
		<post-id xmlns="com-wordpress:feed-additions:1">449922</post-id>	</item>
	</channel>
</rss>
