[paper] Efficient Long-Channel MOSFET Model

Ananda Sankar Chakraborty

Efficient Long-Channel MOSFET Model 

with SPICE-enabled Lambert W Function for Universal Application

Silicon (2025): 1-10; DOI 0.1007/s12633-025-03576-1

1 ETCE, Indian Institute of Engineering Science and Technology, Shibpur (IN)

Abstract: A novel, accurate charge-based MOSFET long-channel computational model is presented, which is portable and can be used across the electrical engineering domains ranging from sensing to power electronics, both under sub-threshold as well as super-threshold regime of MOSFET operation. The proposed physics-based model can be universally used to any long-channel MOS-transistor, as it does not depend on any empirical factor and features extremely good computational efficiency. The model uses a novel two-step charge linearization, resulting into accurate drain current and charge model – valid for both the subthreshold and super-threshold regime of long-channel MOSFET operation. Another salient feature of the proposed model is a novel SPICE-compatible numerical solution strategy for the principal branch of the Lambert W function (W0(x) for {x ∈ R | x ≥ 0}). The algorithm is faster than present industry standard implementations, computationally efficient, accurate with maximum percentage error≈10−14% and therefore may be incorporated in a SPICE engine for electrical design and optimization. The proposed computationally efficient long channel MOSFET model is validated against thorough TCAD simulations upto the fourth derivative and has been found to have fast convergence along with much higher degree of accuracy compared to existing MOSFET models.

FIG: Bulk-MOSFET structure: its current (I_DS) and conductance (g_DS) vs Drain Voltage (V_DS)

(Line: proposed model, symbol: TCAD)

[paper] Bioinspired Phototransistor

Ruyue Han, Dayu Jia, Bo Li, Shun Feng, Guoteng Zhang, Yun Sun, Zheng Han, Chi Liu, Hui-Ming Cheng and Dong-Ming Sun

Bioinspired phototransistor with tunable sensitivity for low-contrast target detection

Light Sci Appl 15, 12 (2026) DOI: 10.1038/s41377-025-02051-1

Shenyang National Laboratory for Materials Science, Institute of Metal Research, CAS, Shenyang (CN)
School of Materials Science and Engineering, University of Science and Technology of China, Shenyang (CN)
School of Information Institution, Liaoning University, Shenyang (CN)
State Key Laboratory of Quantum Optics and Quantum Optics Devices, Shanxi University, Taiyuan (CN)
Collaborative Innovation Center of Extreme Optics, Shanxi University, Taiyuan (CN)
Liaoning Academy of Materials, Shenyang (CN)
Faculty of Materials Science and Engineering, Shenzhen Institute of Advanced Technology, CAS, Shenzhen (CN)

Abstract: Accurate recognition of low-contrast targets in complex visual environments is essential for advanced intelligent machine vision systems. Conventional photodetectors often suffer from a weak photoresponse and a linear dependence of photocurrent on light intensity, which restricts their ability to capture low-contrast features and makes them susceptible to noise. Inspired by the adaptive mechanisms of the human visual system, we present a molybdenum disulfide (MoS2) phototransistor with tunable sensitivity, in which the gate stack incorporates a heterostructure diode—composed of O-plasma-treated MoS2 and pristine MoS2—that serves as the photosensitive layer. This configuration enables light-intensity-dependent modulation of the diode’s conductance, which dynamically in turn alters the voltage distribution across the gate dielectric and transistor channel, leading to a significant photoresponse. By modulating the gate voltage, the light response range can be finely tuned, maintaining high sensitivity to low-contrast targets while suppressing noise interference. Compared to conventional photodetectors, the proposed device achieves a 1000-fold improvement in sensitivity for low-contrast signal detection and exhibits significantly enhanced noise immunity. The intelligent machine vision system built on this device demonstrates exceptional performance in detecting low-contrast targets, underscoring its promise for next-generation machine vision applications.

FIG: Performance of tunable-sensitivity phototransistor array. (a) Optical image of a 3 × 3 phototransistor array (scale bar: 200 μm). (b) Magnified image of an individual sensor unit (scale bar: 10 μm). 

(c) IDS−VGS curves of the 9 phototransistors in dark and under 516-nm light at VDS = 0.1 V. 

Acknowledgements: This work was supported by the National Key Research and Development Program of China (2021YFA1200801), the National Natural Science Foundation of China (No. 62304226, 52188101, 62450124, 62125406), the China Postdoctoral Science Foundation (2024T170946, 2023M733574), the Excellent Youth Fund Project of Liaoning Province (2023JH3/10200003), the Outstanding Youth Fund Project of Liaoning Province (2025JH6/101100015), the Special Projects of the Central Government in Guidance of Local Science and Technology Development (2024010859-JH6/1006), the Special Research Assistantship Project of the Chinese Academy of Sciences (E455L502), the China Postdoctoral Science Foundation under Grant Number GZB20230776, the Liaoning Provincial Key Laboratory of Public Opinion and Network Security Information System (d252453002), the Artificial Intelligence Technology Innovation Project of Liaoning Province (Grant No. 2023JH26/10300019), the Young Top-notch Talents of the National High-level Talent Special Support Program, the basic scientific research project of universities funded by the Liaoning Provincial Department of Education (LJ212510140016) and the Liaoning Province High-quality Industry-University Cooperation and Collaborative Education Project (241201160090747). The authors gratefully acknowledge Dr. Bing Yang and Dr. Honglei Chen from the Institute of Metal Research for their valuable support in HRTEM-EDS characterization.