International Scholarly Research Network ISRN Electronics Volume 2012, Article ID 148492, 5 pages doi:10.5402/2012/148492 Research Article Fully Programmable Gaussian Function Generator Using Floating Gate MOS Transistor Richa Srivastava, Maneesha Gupta, and Urvashi Singh Electronics and Communication Engineering Department, NSIT, New Delhi 110078, India Correspondence should be addressed to Richa Srivastava, richa ec@yahoo.co.in Received 29 September 2012; Accepted 23 October 2012 Academic Editors: J.-M. Kwon and A. L. P. Rotondaro Copyright © 2012 Richa Srivastava et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Floating gate MOS (FGMOS) based fully programmable Gaussian function generator is presented. The circuit combines the tunable property of FGMOS transistor, exponential characteristics of MOS transistor in weak inversion, and its square law characteristic in strong inversion region to implement the function. Two-quadrant current mode squarer is the core subcircuit of Gaussian function generator that helps to implement full Gaussian function for positive as well as negative input current. FGMOS implementation of the circuit reduces the current mismatching error and increases the tunability of the circuit. The performance of circuit is verified at 1.8 V in TSMC 0.18 μm CMOS, BSIM3, and Level 49 technology by using Cadence Spectre simulator. 1. Introduction Gaussian function is one of the most widely used functions in many domains such as neural network, neural algo- rithm, and on-chip diffusion profile. Diffusion is one of the important steps in the chip fabrication. The diffusion profile of impurity atoms is dependent on the initial and boundary conditions. When a constant amount of dopant is deposited on the surface, the doping profile is approximated by Gaussian function [1]. Another application of Gaussian function is observed in multidimensional problems like pattern matching and data classifications [2]. These cases are calculated using probability density functions and these functions can be modeled by normal distributions [3]. Madrinas et al. proposed a CMOS analog integrated circuit to implement Gaussian function [4]. They have successfully designed a five-transistor circuit in which current mirror is in weak inversion region and voltage variable resistors are replaced by two MOS transistors. But the conventional circuit has limitations of mismatching of MOS transistors and it can implement only half of the Gaussian function. The circuit proposed in [5] overcomes the limitations of the circuit proposed in [4] by using FGMOS transistors. Recently the work published in [6] implements the Gaussian functions using fourth-order approximation. The accuracy and complexity of this circuit depends upon order of approximation of the Gaussian function. So, there is always a tradeoff between circuit complexity and its accuracy. This paper presents very simple FGMOS based fully programmable Gaussian function generator that uses a single two-quadrant current mode squarer/divider to generate fully programmable Gaussian function. FGMOS has many attractive features for example it reduces the complexity of circuits and can simplify the signal processing chain of a design. It can shift the signal levels and incorporate tunable mechanisms. It can even work normally below the operational limits of supply voltage levels for a particular technology and thus consume less power than the minimum power required for a MOS circuit of same technology without affecting the performance of the device [7]. The paper is organized as follows. Basics of FGMOS transistor is given in Section 2. A fully programmable Gaussian function generator is introduced and analyzed in Section 3. Next section details the simulation results. Finally on the basis of simulation results, conclusions are drawn in the last section.