Robustness

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A Bidirectional Neuromodulation Chipset With Algorithm-Aware AFE Optimization and High-Voltage-Compliant Stimulation

A Bidirectional Neuromodulation Chipset With Algorithm-Aware AFE Optimization and High-Voltage-Compliant Stimulation 150 150
Author(s): Xiaohua Huang, Hoda Yassin, Beatriz Lafuente Alcazar, Arash Akhoundi, Dante G. Muratore

Abstract:

This work presents a bidirectional neuromodulation chipset with 64-channel neural analog front-end (AFE), and a four-channel current stimulator. The chipset employs a heterogeneous architecture, combining a 28-nm low-voltage (LV) CMOS process for the AFE and the digital backend (DBE) to improve area and power efficiency, with a 180-nm high-voltage (HV) …

Published in: IEEE Journal of Solid-State Circuits
Page(s): 1 – 13
Year of Publication: 2026
Electronic ISSN: 1558-173X
DOI: 10.1109/JSSC.2025.3650066
Publisher: IEEE

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A 92.1-dB SNDR Easy-Driving Two-Step NS-SAR-Based Incremental ADC With Concurrent Gain-Error Plus Noise Suppression

A 92.1-dB SNDR Easy-Driving Two-Step NS-SAR-Based Incremental ADC With Concurrent Gain-Error Plus Noise Suppression 150 150
Author(s): Chenyang Li, Tzu-Han Wang, Dongsuk Kang, Ken Li, Xitie Zhang, Wei-En Lee, Visvesh S. Sathe, Shaolan Li

Abstract:

This article presents a two-step incremental analog-to-digital converter (ADC) that achieves high resolution and energy efficiency while substantially easing the input driving constraints and interstage gain variation. By employing a level-shifted sub-ranging architecture with an input-tracking (IT) feature, the design obviates direct input sampling, thereby significantly relaxing the demands on …

Published in: IEEE Journal of Solid-State Circuits
Page(s): 1 – 14
Year of Publication: 2025
Electronic ISSN: 1558-173X
DOI: 10.1109/JSSC.2025.3635146
Publisher: IEEE

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1.58-b FeFET-Based Ternary Neural Networks: Achieving Robust Compute-In-Memory With Weight-Input Transformations

1.58-b FeFET-Based Ternary Neural Networks: Achieving Robust Compute-In-Memory With Weight-Input Transformations 150 150
Author(s): Imtiaz Ahmed, Akul Malhotra, Revanth Koduru, Sumeet Kumar Gupta

Abstract:

Ternary weight neural networks (TWNs), with weights quantized to three states (−1, 0, and 1), have emerged as promising solutions for resource-constrained edge artificial intelligence (AI) platforms due to their high energy efficiency with acceptable inference accuracy. Further energy savings can be achieved with TWN accelerators utilizing techniques such as compute-in-memory (CiM) and …

Published in: IEEE Journal on Exploratory Solid-State Computational Devices and Circuits
Page(s): 157 – 165
Year of Publication: 2025
Electronic ISSN: 2329-9231
DOI: 10.1109/JXCDC.2025.3621160
Publisher: IEEE

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