Bibliographic Details
| Title: |
Design and Large Signal Analysis of a 90 nm Novel CMOS Operational Trans-Resistance Amplifier. |
| Authors: |
Gupta, Amit1 (AUTHOR) amitgupta.vlsi@gmail.com, Raman, Ashish1 (AUTHOR) ramana@nitj.ac.in |
| Source: |
Circuits, Systems & Signal Processing. Jan2026, Vol. 45 Issue 1, p278-298. 21p. |
| Subjects: |
Complementary metal oxide semiconductors, CMOS amplifiers, Transistor circuits, Electric circuit analysis, Simulation methods & models |
| Abstract: |
The paper presents a new 18-transistor full CMOS operational trans-resistance amplifier (OTRA) circuit implemented in tsmc_90nm CMOS technology. The proposed circuit permits independent control of small signal input resistance and common mode rejection ratio (CMRR) for a given small signal differential gain. The OTRA offers large open loop trans-resistance gain combined with large CMRR and low input resistance. The large signal analysis of the circuit has been presented and simple approximate expressions for the small signal gains and input resistances have been derived. The schematic and the post layout simulation results for the circuit have been compared with those predicted by the theoretical analysis. An excellent agreement is observed between the simulation results and the corresponding theoretical values. It has been shown that the theoretically derived equations can be used for design of the OTRA. The process corner analysis of the circuit has been carried out for two extreme process corners at four different temperatures and three different power supply voltages to determine the sensitivity of the OTRA parameters to the PVT variations. The performance of the proposed OTRA has been compared with the same for earlier reported OTRA designs. [ABSTRACT FROM AUTHOR] |
|
Copyright of Circuits, Systems & Signal Processing is the property of Springer Nature and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) |
| Database: |
Engineering Source |