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Rahul Goyal

Publications and source records attributed to Rahul Goyal.

3 recordsLinked to original sources

Spatiotemporal Thermal Modulation and Patterning using a Programmable 1024 Element Microheater Array

Programmable microheater arrays are essential for a variety of applications including gas sensing, microfluidic lab on a chip devices, 3D printers, and biosensors that rely on DNA amplification. Increasing the density and number of heating elements directly correlates with the precision with which spatiotemporal heat profiles can be delivered. However, large arrays have thus far not been realized. One challenge is that as the number of elements in an array increases, the complexity of connecting them grows. Here, we show that row-column addressing provides a promising architecture for the efficient operation of a large micro-heater array. We introduce a programmable 32 x 32 microheater array consisting of individually addressable robust platinum (Pt)-based Joule heating elements- each smaller than 300 micrometer. We show that combining high-voltage multiplexed electronics and sequential addressing controlled by a high frequency clock, allows the independent operation of the 1024 microheater elements. We demonstrate the generation of heat images and the patterning of metallic structures formed from the liquid metal Gallium. Our work demonstrates new capabilities for on-chip thermal devices, and opens the possibility to realize novel heat-controlled microactuation systems.

cond-mat.soft

Low-power scalable multilayer optoelectronic neural networks enabled with incoherent light

Optical approaches have made great strides towards the goal of high-speed, energy-efficient computing necessary for modern deep learning and AI applications. Read-in and read-out of data, however, limit the overall performance of existing approaches. This study introduces a multilayer optoelectronic computing framework that alternates between optical and optoelectronic layers to implement matrix-vector multiplications and rectified linear functions, respectively. Our framework is designed for real-time, parallelized operations, leveraging 2D arrays of LEDs and photodetectors connected via independent analog electronics. We experimentally demonstrate this approach using a system with a three-layer network with two hidden layers and operate it to recognize images from the MNIST database with a recognition accuracy of 92% and classify classes from a nonlinear spiral data with 86% accuracy. By implementing multiple layers of a deep neural network simultaneously, our approach significantly reduces the number of read-ins and read-outs required and paves the way for scalable optical accelerators requiring ultra low energy.

cs.ET

Optimization and Neural Network-Based Modelling of Surface Passivation Effectiveness by Hydrogenated Amorphous Silicon for Solar Cell Applications

Intrinsic hydrogenated amorphous silicon films can provide outstanding surface passivation of crystalline silicon wafer surfaces. This quality of Intrinsic hydrogenated amorphous silicon makes it valuable in heterojunction with intrinsic thin layer (HIT) solar cell fabrication. This paper describes the material characteristics and electronic properties of Intrinsic hydrogenated amorphous silicon that affects its passivation quality. A study of passivation quality of intrinsic hydrogenated amorphous silicon layer has been done with respect to deposition parameters in Plasma Enhanced Chemical Vapor Deposition (PECVD), the most commonly used method of its deposition. It was found that very good surface passivation with surface recombination velocity < 50 cm/s can be obtained from thickness of 30 nm of Intrinsic hydrogenated amorphous silicon (a-Si:H(i)), which is better than most other passivation techniques. A mathematical model based on Artificial Neural Network (ANN) is designed to predict the carrier lifetime for a given deposition condition and it is shown that the prediction capability of developed ANN model varies with the number of neurons in the hidden layer using Akaike Information Criterion (AIC), which is a widely accepted model selection method for measuring the validity of nonlinear models.

physics.app-ph