<?xml version="1.0" encoding="utf-8" standalone="yes"?><rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>Neural Interface | Lin's Lab</title><link>https://www.linqiuyang.com/en/tag/neural-interface/</link><atom:link href="https://www.linqiuyang.com/en/tag/neural-interface/index.xml" rel="self" type="application/rss+xml"/><description>Neural Interface</description><generator>Hugo Blox Builder (https://hugoblox.com)</generator><language>en</language><lastBuildDate>Mon, 31 Aug 2026 00:00:00 +0000</lastBuildDate><image><url>https://www.linqiuyang.com/media/icon_hu17276893506282036950.png</url><title>Neural Interface</title><link>https://www.linqiuyang.com/en/tag/neural-interface/</link></image><item><title>Paper Review: A 2.5–20 kS/s In-Pixel Direct Digitization ECoG Front End With Submillisecond Stimulation Artifact Recovery</title><link>https://www.linqiuyang.com/en/research/review/neural-interface-ics/ecog_artifact_recovery/</link><pubDate>Mon, 31 Aug 2026 00:00:00 +0000</pubDate><guid>https://www.linqiuyang.com/en/research/review/neural-interface-ics/ecog_artifact_recovery/</guid><description>&lt;h2 id="paper">Paper&lt;/h2>
&lt;p>&lt;strong>A 2.5–20 kS/s In-Pixel Direct Digitization ECoG Front End With Submillisecond Stimulation Artifact Recovery&lt;/strong>&lt;/p>
&lt;p>&lt;strong>Reviewer:&lt;/strong> Yiru Wang&lt;/p>
&lt;p>&lt;strong>Full review:&lt;/strong> &lt;a href="https://www.linqiuyang.com/research/review/neural-interface-ics/ecog_artifact_recovery/paper.pdf">View the Review Document (PDF)&lt;/a>&lt;/p>
&lt;hr>
&lt;h2 id="introduction">Introduction&lt;/h2>
&lt;p>This paper presents an eight-channel ECoG recording front end fabricated in &lt;strong>180 nm CMOS&lt;/strong>. A second-order continuous-time ΔΣ modulator and a decimation filter are integrated within each pixel, supporting four recording modes from &lt;strong>2.5 to 20 kS/s&lt;/strong>.&lt;/p>
&lt;p>Rather than relying solely on a wider input dynamic range or a complex artifact-cancellation path, this work addresses the &lt;strong>phase wrapping and slow recovery&lt;/strong> that can occur when a large stimulation artifact overloads a time-domain quantizer. A fast-recovery phase quantizer with overrange detection allows the system to return to stable recording after a brief period of saturation.&lt;/p>
&lt;hr>
&lt;h2 id="key-features">Key Features&lt;/h2>
&lt;ul>
&lt;li>In-pixel second-order &lt;strong>CT-ΔΣ ADC&lt;/strong> for direct digitization of neural signals&lt;/li>
&lt;li>&lt;strong>Pseudo-Virtual-Ground Feedforward (PVG FF)&lt;/strong> architecture for reduced DAC overhead and improved linearity and area efficiency&lt;/li>
&lt;li>Chopper-stabilized complementary-input &lt;strong>Gm-C integrator&lt;/strong> as the first stage&lt;/li>
&lt;li>&lt;strong>CCO-based&lt;/strong> time-domain integrator as the second stage&lt;/li>
&lt;li>Fast-recovery phase quantizer with overrange detection to prevent phase wrapping and modulator instability&lt;/li>
&lt;li>Four recording modes at 2.5, 5, 10, and 20 kS/s, with proportional power scaling across bandwidths&lt;/li>
&lt;li>Per-pixel third-order &lt;strong>CIC decimation filter&lt;/strong> providing 64× data-rate reduction&lt;/li>
&lt;li>In vivo rat experiments demonstrating simultaneous stimulation and recording with rapid post-artifact recovery&lt;/li>
&lt;/ul>
&lt;hr>
&lt;h2 id="performance-summary">Performance Summary&lt;/h2>
&lt;table>
&lt;thead>
&lt;tr>
&lt;th style="text-align: left">Parameter&lt;/th>
&lt;th style="text-align: right">Performance&lt;/th>
&lt;/tr>
&lt;/thead>
&lt;tbody>
&lt;tr>
&lt;td style="text-align: left">Technology&lt;/td>
&lt;td style="text-align: right">180 nm CMOS&lt;/td>
&lt;/tr>
&lt;tr>
&lt;td style="text-align: left">Array Size&lt;/td>
&lt;td style="text-align: right">4 × 2 (8 channels)&lt;/td>
&lt;/tr>
&lt;tr>
&lt;td style="text-align: left">ADC Architecture&lt;/td>
&lt;td style="text-align: right">In-pixel second-order CT-ΔΣ ADC&lt;/td>
&lt;/tr>
&lt;tr>
&lt;td style="text-align: left">Output Sampling Rate&lt;/td>
&lt;td style="text-align: right">2.5–20 kS/s&lt;/td>
&lt;/tr>
&lt;tr>
&lt;td style="text-align: left">Signal Bandwidth&lt;/td>
&lt;td style="text-align: right">1.25–10 kHz&lt;/td>
&lt;/tr>
&lt;tr>
&lt;td style="text-align: left">Pixel Area&lt;/td>
&lt;td style="text-align: right">0.09 mm²/pixel&lt;/td>
&lt;/tr>
&lt;tr>
&lt;td style="text-align: left">Power Consumption&lt;/td>
&lt;td style="text-align: right">14 µW/pixel&lt;/td>
&lt;/tr>
&lt;tr>
&lt;td style="text-align: left">Analog / Digital Supply&lt;/td>
&lt;td style="text-align: right">0.9 V / 0.7 V&lt;/td>
&lt;/tr>
&lt;tr>
&lt;td style="text-align: left">Input-Referred Noise&lt;/td>
&lt;td style="text-align: right">6 µVrms&lt;/td>
&lt;/tr>
&lt;tr>
&lt;td style="text-align: left">ADC SNDR / DR&lt;/td>
&lt;td style="text-align: right">78.6 dB / 78.6 dB&lt;/td>
&lt;/tr>
&lt;tr>
&lt;td style="text-align: left">ADC SFDR&lt;/td>
&lt;td style="text-align: right">97.7 dBc&lt;/td>
&lt;/tr>
&lt;tr>
&lt;td style="text-align: left">Stimulation Artifact Recovery Time&lt;/td>
&lt;td style="text-align: right">0.05–0.4 ms&lt;/td>
&lt;/tr>
&lt;tr>
&lt;td style="text-align: left">In-Pixel Decimation Filter&lt;/td>
&lt;td style="text-align: right">Third-order CIC, 64×&lt;/td>
&lt;/tr>
&lt;/tbody>
&lt;/table>
&lt;hr>
&lt;h2 id="summary">Summary&lt;/h2>
&lt;p>This work is a strong example of a &lt;strong>fast-recovery direct-digitization neural front end&lt;/strong>. Its key contribution is not to keep every large stimulation artifact within the ADC input range. Instead, it prevents phase wrapping in the time-domain quantizer after an overrange event, allowing normal recording to resume within one decimated output sample.&lt;/p>
&lt;p>The design also combines scalable Gm-C and CCO circuits, per-pixel decimation, and distributed timing generation to support multiple recording bandwidths, proportional power scaling, and future expansion toward large-scale arrays.&lt;/p>
&lt;p>For a detailed discussion of the PVG FF architecture, Gm-C integrator, CCO, fast-recovery phase quantizer, and digital back end, please refer to the &lt;strong>&lt;a href="https://www.linqiuyang.com/research/review/neural-interface-ics/ecog_artifact_recovery/paper.pdf">full review document&lt;/a>&lt;/strong>.&lt;/p></description></item><item><title>Review: A Highly-Integrated 1536-Channel Quad-Shank Monolithic Neural Probe in 55nm CMOS for Full-Band Raw-Signal Recording</title><link>https://www.linqiuyang.com/en/research/review/neural-interface-ics/1536-channel-quad-shank-neural-probe/</link><pubDate>Mon, 24 Aug 2026 00:00:00 +0000</pubDate><guid>https://www.linqiuyang.com/en/research/review/neural-interface-ics/1536-channel-quad-shank-neural-probe/</guid><description>&lt;h2 id="paper">Paper&lt;/h2>
&lt;p>&lt;strong>A Highly-Integrated 1536-Channel Quad-Shank Monolithic Neural Probe in 55nm CMOS for Full-Band Raw-Signal Recording&lt;/strong>&lt;/p>
&lt;p>&lt;strong>Review by:&lt;/strong> 王奕如&lt;/p>
&lt;p>&lt;strong>Full Review:&lt;/strong> &lt;a href="https://www.linqiuyang.com/research/review/neural-interface-ics/1536-channel-quad-shank-neural-probe/paper.pdf">View Review Document (PDF)&lt;/a>&lt;/p>
&lt;hr>
&lt;h2 id="overview">Overview&lt;/h2>
&lt;p>This work presents a &lt;strong>1536-channel quad-shank monolithic neural probe&lt;/strong> fabricated in 55 nm CMOS, supporting simultaneous recording from up to &lt;strong>1536 out of 5120 electrodes&lt;/strong>.&lt;/p>
&lt;p>Rather than focusing solely on minimum area or power per channel, the work addresses system-level trade-offs in large-scale neural recording, including &lt;strong>electrode density, noise, power, area, multiplexing, and data readout&lt;/strong>.&lt;/p>
&lt;hr>
&lt;h2 id="key-features">Key Features&lt;/h2>
&lt;ul>
&lt;li>&lt;strong>5120 electrodes / 1536 simultaneous recording channels&lt;/strong>&lt;/li>
&lt;li>Conventional &lt;strong>IA–buffer–MUX–SAR ADC&lt;/strong> signal chain&lt;/li>
&lt;li>Dynamically biased buffers for reduced power&lt;/li>
&lt;li>96 shared &lt;strong>12-bit SAR ADCs&lt;/strong>&lt;/li>
&lt;li>Shared ADC reference buffers&lt;/li>
&lt;li>Full-band recording of both AP and LFP signals&lt;/li>
&lt;/ul>
&lt;hr>
&lt;h2 id="key-results">Key Results&lt;/h2>
&lt;table>
&lt;thead>
&lt;tr>
&lt;th style="text-align: left">Parameter&lt;/th>
&lt;th style="text-align: right">Performance&lt;/th>
&lt;/tr>
&lt;/thead>
&lt;tbody>
&lt;tr>
&lt;td style="text-align: left">Technology&lt;/td>
&lt;td style="text-align: right">55 nm CMOS&lt;/td>
&lt;/tr>
&lt;tr>
&lt;td style="text-align: left">Recording Electrodes&lt;/td>
&lt;td style="text-align: right">5120&lt;/td>
&lt;/tr>
&lt;tr>
&lt;td style="text-align: left">Simultaneous Recording Channels&lt;/td>
&lt;td style="text-align: right">1536&lt;/td>
&lt;/tr>
&lt;tr>
&lt;td style="text-align: left">Probe Structure&lt;/td>
&lt;td style="text-align: right">Quad-shank&lt;/td>
&lt;/tr>
&lt;tr>
&lt;td style="text-align: left">ADC&lt;/td>
&lt;td style="text-align: right">12-bit SAR&lt;/td>
&lt;/tr>
&lt;tr>
&lt;td style="text-align: left">Channel Area&lt;/td>
&lt;td style="text-align: right">0.012 mm²/ch&lt;/td>
&lt;/tr>
&lt;tr>
&lt;td style="text-align: left">Total Power&lt;/td>
&lt;td style="text-align: right">29.7 mW&lt;/td>
&lt;/tr>
&lt;tr>
&lt;td style="text-align: left">Power / Channel&lt;/td>
&lt;td style="text-align: right">19.34 µW/ch&lt;/td>
&lt;/tr>
&lt;tr>
&lt;td style="text-align: left">AP Input-Referred Noise&lt;/td>
&lt;td style="text-align: right">6.01 ± 0.39 µVrms&lt;/td>
&lt;/tr>
&lt;tr>
&lt;td style="text-align: left">LFP Input-Referred Noise&lt;/td>
&lt;td style="text-align: right">7.58 ± 0.79 µVrms&lt;/td>
&lt;/tr>
&lt;tr>
&lt;td style="text-align: left">ADC SNDR&lt;/td>
&lt;td style="text-align: right">64.92 dB&lt;/td>
&lt;/tr>
&lt;/tbody>
&lt;/table>
&lt;hr>
&lt;h2 id="takeaway">Takeaway&lt;/h2>
&lt;p>This work provides a representative example of &lt;strong>system-level optimization for high-density neural recording&lt;/strong>, balancing channel density, noise, power, area, and system scalability.&lt;/p>
&lt;p>It also demonstrates that the conventional &lt;strong>IA + MUX + SAR ADC&lt;/strong> architecture remains highly competitive for large-scale neural recording when carefully optimized.&lt;/p>
&lt;p>For detailed discussions of the pixel architecture, instrumentation amplifier, dynamic-bias buffer, SAR ADC, and reference buffer design, please refer to the &lt;strong>&lt;a href="https://www.linqiuyang.com/research/review/neural-interface-ics/1536-channel-quad-shank-neural-probe/paper.pdf">full review document&lt;/a>&lt;/strong>.&lt;/p></description></item></channel></rss>