# Electricity supply quality and use among rural and peri-urban households and
small firms in Nigeria

Setu Pelz  International Institute for Applied Systems Analysis, Laxenburg,
Austria  Off-Grid Systems, Reiner Lemoine Institut (RLI), Berlin, Germany
Narges Chinichian  Off-Grid Systems, Reiner Lemoine Institut (RLI), Berlin,
Germany  Institute for Theoretical Physics, Technical University of Berlin,
Germany  Clara Jütte  Off-Grid Systems, Reiner Lemoine Institut (RLI), Berlin,
Germany  Philipp Blechinger  Off-Grid Systems, Reiner Lemoine Institut (RLI),
Berlin, Germany

###### Abstract

We present a household and enterprise energy survey dataset collected within
the framework of the PeopleSuN project in Nigeria in 2021. Across three
Nigerian geopolitical zones, a total of 3,599 households and 1,122 small and
medium-sized enterprises were surveyed. The sample is designed to be
representative of rural and peri-urban grid-electrified regions of each zone.
Our surveys collect data on demographic and socioeconomic characteristics,
energy access and supply quality, electrical appliance ownership and usage
time, cooking solutions, energy related capabilities, and supply preferences.
We encourage academic use of the data presented and suggest three avenues of
further research: (1) modelling appliance ownership likelihoods, electricity
consumption levels and energy service needs in un-electrified regions; (2)
identifying supply-side and demand-side solutions to address high usage of
diesel generators; (3) exploring broader issues of multi-dimensional energy
access, access to decent living standards and climate vulnerability.

## Background & Summary

Rural and peri-urban populations in Nigeria continue to suffer unreliable and
expensive energy supply. According to the World Bank, the electricity access
rate in Nigeria stood at 55.4% in 2020 with a big gap between urban and rural
areas (83.9% vs. 24.6%)[WorldBank2020]. At the same time, nearly 30 million
Nigerian households depend on wood as a source of fuel, the collection of
which is time consuming and mainly done by women[world2022world]. Where there
is supply, it is typically unreliable and frequently interrupted by blackouts.
The Nigeria Enterprise Survey from the World Bank showed that 27% of Nigerian
firms identified reliability of electricity supply as the main obstacle to
their business[WBES2014]. On average, 32.8 power outages were reported to
occur in a typical month leading to an estimated 11% loss in sales
value[WBES2014]. The average grid-connected household receives just 6.6 hours
of supply on a typical day, linked to a per capita consumption of just 144kWh
per year[LSMS2019]. In comparison, the annual per capita consumption in Ghana
and South Africa is respectively 351 kWh and 4,198 kWh. Plagued by issues of
supply quality, many Nigerians have resorted to self-generation using petrol
and diesel generators, spending approximately 1.56 trillion Naira (3.76
billion USD111Average exchange rate in 2021: 1 Naira = 0.0024 USD) per year on
fuel [Ugwoke2020].

A central motivation for improving energy supply is to provide the means to
secure access to basic energy service needs [fell_energy_2017]. These needs
include examples such as ‘being at a comfortable temperature’ or ‘being able
to store food’. These are closely linked with the broader concept of ‘basic
human needs’ [doyal_theory_1991] and have been used to define minimum material
(energy) requirements under the Decent Living Standards [rao_decent_2018].
Data describing satisfaction of such needs is severely limited to non-
existent, and as we show through this data release, cannot be conflated with
mere access to a modern energy carrier. An analysis of household access to
modern energy carriers and supply quality must therefore be complemented by an
analysis of satisfaction of a set of basic energy services. Indeed this notion
of energy service needs is a foundational element of the World Bank’s multi-
tier framework for measuring energy access [Bhatia2015, ESMAP2020]. Moreover,
an understanding of energy service needs and preferences is especially
important for planning off-grid electrification measures where appliances and
energy carriers are typically bundled, forming energy service delivery
systems. Several studies and analyses have emphasized that in order for
Nigeria to reach its access targets while also increasing the proportion of
renewable energy in its energy mix, a combination of grid extension and off-
grid systems, e.g. mini-grids, and solar home systems (SHS) is necessary
[IEA2019, esmap2022, SE4All2022].

While global efforts are accelerating under the banner of achieving
Sustainable Development Goal 7 (SDG7) by 2030, stakeholder discussions
indicate that progress in Nigeria is hindered by data availability, among
other barriers. Data describing the energy access deficit in Nigeria exists
(see Table 1), however, there is limited disaggregate information describing
the supply quality in the existing network and the unmet demand in ‘un-
electrified’ regions. In this paper we present survey data collected to fill
this and other gaps through the ‘People Power: Optimizing off-grid electricity
supply systems in Nigeria’ project (PeopleSuN)222PeopleSuN is funded by the
German Federal Ministry of Education and Research (BMBF) within the funding
initiative ‘Client II - International Partnerships for Sustainable
Innovations’.. The methods applied draw on stakeholder discussions conducted
in Nigeria to define the data gap and the necessary survey and sampling
strategy to address this. The questionnaires used draw from specific modules
within established surveys capturing energy-related data, most directly from
the Multi-Tier Framework for Measuring Energy Access surveys [Bhatia2015]. The
surveys provide data on household and enterprise characteristics, energy
supply and consumption. They also capture preferences, trust in institutions
and several gender-disaggregated variables. Our sample is representative of
grid-electrified rural and peri-urban regions across three geopolitical zones
with large energy access deficits. This data provides important insight into
actual energy supply quality and use among ‘electrified’ communities and can
be used to improve models of energy demand in similar but currently ‘un-
electrified’ communities. The final sample includes 3,599 households and 1,122
small and medium sized enterprises from 225 enumeration areas across the three
geopolitical zones.

Data source |  Description |  Year |  Representativeness |  Energy data
---|---|---|---|---
General Household Survey / Living Standards Measurement Study (World Bank) https://www.worldbank.org/en/programs/lsms |  National household living standards survey. |  Multiple survey rounds (most recent 2018) |  Nationally representative survey of approximately 5,000 households, which are also representative of the six geopolitical zones. |  Household socioeconomics, assets and general energy access questions.
Nigeria SE4ALL Platform. Federal Ministry of Power, Nigeria. https://nigeriase4all.gov.ng/ |  Platform provides access to geospatial data and market data related to grid-connected and off-grid energy in Nigeria. |  Frequently updated. |  Geospatial data for all of Nigeria. |  Spatial data describing locations of consumers, existing and planned grid and off-grid energy infrastructure, and administrative boundaries.
Energy Sector Management Assistance Program (ESMAP), World Bank. Nigeria - Multi-Tier Framework for Measuring Energy Access Household Survey (MTF) 2018. https://microdata.worldbank.org/index.php/catalog/3865 |  The MTF survey assesses energy access and use in North, North-West Nigeria. |  2018 |  Stratified random sampling, with equal allocation between urban and rural areas and equal allocation between grid-user and non grid-user households in North, North-West of Nigeria. |  Data on energy access, types of energy sources used, appliance ownership and energy expenditure.
Falchetta, G., Pachauri, S., Parkinson, S. et al. A high-resolution gridded dataset to assess electrification in sub-Saharan Africa. Sci Data 6, 110 (2019). https://doi.org/10.1038/s41597-019-0122-6 |  A high-resolution gridded dataset to assess electrification in sub-Saharan Africa |  2019 |  sub-Saharan Africa |  Spatial distribution and temporal evolution of electricity access.
DHS surveys - Nigeria https://dhsprogram.com/ |  Demographic and Health Surveys collect data on population, health, and nutrition in Nigeria. |  Multiple survey rounds (most recent 2018) |  Nationally representative, with data available for individual states and regions. |  Data on household electricity access, types of cooking fuels, and ownership of appliances.
World Bank Enterprise Surveys for Nigeria https://www.enterprisesurveys.org/ |  Enterprise surveys provide data on the business environment, including energy-related aspects, through interviews with firm managers and owners. |  Periodically conducted, most recent in 2014. |  Covers various regions in Nigeria with a focus on the formal sector. |  Energy reliability, power outages, and energy expenditures for businesses.

Table 1: Overview of existing sources of data describing energy access and use
in rural and peri-urban Nigeria.

## Methods

Figure 1 provides an overview of the sample design and survey modules used.
Complete codebooks describing the questionnaires are available here:
https://dataverse.harvard.edu/dataset.xhtml?persistentId=doi:10.7910/DVN/GTNEJD.
We now describe the data collection methods in detail.

Figure 1: Sample design and questionnaire modules. Three of six Nigerian
zones, North West, North Central and South South were selected. These zones
were chosen based on lower urbanisation rates (excluding the South West and
South East) and safety and logistical considerations (excluding North East).
Seventy five enumeration areas (EA) were marked in each zone [total of 225
EAs]. Within each enumeration area, sixteen households and five enterprises
were planned to be surveyed. The desired distribution of EAs and the final
implementation are shown respectively in Table 4. The surveys cover a wide
range of questions regarding demographic and socioeconomic variables,
electricity availability and supply quality, appliance ownership and use,
available cooking solutions, capabilities and preferences.

### Sample frame

Three geopolitical zones were selected for sampling: North West, North
Central, and South South. These zones were purposefully chosen based on lower
rates of urbanization (excluding South West and South East) and safety and
associated logistical concerns (excluding North East). The sample frame was
then developed using remotely sensed datasets due to the lack of up-to-date
administrative data. Four remotely sensed spatial datasets were used, as shown
in Table 2. The focus of this work was on understanding energy supply quality
and use among grid-electrified households living in rural and peri-urban
regions (outside urban centers). Urbanization was defined using the ‘Urban
Rural Catchment Area’ dataset (URCA), which stratified the population by
travel time to the nearest agglomeration. Further detail on the URCA sub-
categories and their definition can be found in [Cattaneo2021]. For the
purposes of this work, the URCA categories were aggregated into an Urban-core
and varying degrees of rurality, as shown in Table 3. Figure 2 describes the
distribution of households across these categories and separated into those
assumed to be electrified as per the ELEC dataset. All Towns (>0.02M and
<0.05M population) and regions that were within or further than 1 hour travel
time from a city boundary were defined as outside the urban core and relevant
to this study. Estimated population sizes and electrification rates within
each aggregate spatial category across all six geopolitical zones are shown in
Figure 2, providing further intuition regarding the selection of North
Central, North West, and South South as zones of study.

Acronym |  Detail |  Resolution / Year |  Source
---|---|---|---
(WPOP) |  WorldPop Bottom-up Population Estimate |  0.1km2, 2020 |  [Bondarenko2020]
(URCA) |  Urban-Rural Catchment Area |  1km2, 2015 |  [Cattaneo2021]
(ELEC) |  AtlasAI Electrification Data |  4km2, 2020 |  (see atlasai.com)
(ASSET) |  AtlasAI Asset Index |  4km2, 2020 |  (see atlasai.com)

Table 2: Overview of remotely sensed datasets used in defining the sample
frame.

| Large cities | Intermediate cities | Small cities | Towns
---|---|---|---|---
>5M | 1-5M | 0.5-1M | 0.25-0.5M | 0.1-0.25M | 0.05-0.1M | 0.02-0.05M
Urban centers | 1 | 2 | 3 | 4 | 5 | 6 | 7
Peri-urban | 0-1 hours | 8 | 9 | 10 | 11 | 12 | 13 | 20a
Peri-rural | 1-2 hours | 14 | 15 | 16 | 17 | 18 | 19 | 20b
2-3 hours | 21 | 22 | 23 | 24 | 25 | 26 | 27
Hinterlands | >3 hours |  |  |  |  |  |  | 28/29

(a) URCA original categories

| Large cities | Intermediate cities | Small cities | Towns
---|---|---|---|---
| >5M | 1-5M | 0.5-1M | 0.25-0.5M | 0.1-0.25M | 0.05-0.1M | 0.02-0.05M
Urban centers | Urban Core | Cat 1
Peri-urban | 0-1 hours | Cat 2 | Cat 3 | Cat 4
| 1-2 hours | Cat 4
Peri-rural | 2-3 hours | Cat 4
Hinterlands | >3 hours |  |  |  |  |  |  | Cat 4

(b) URCA PeopleSuN aggregated categories

Table 3: Original URCA spatial categories and corresponding PeopleSuN
aggregated categories. URCA categories reflect distinct catchment areas
describing distance from urban agglomerations taking into account road quality
among other factors. These are aggregated for the purposes of the PeopleSuN
sample. See [Cattaneo2021] for further detail regarding the categorisation
approach. Figure 2: Distribution of households and share electrified by
rurality across Nigeria. Population estimates are taken from the latest
bottom-up WorldPop dataset for Nigeria (V1.2, see [Bondarenko2020]). These
were converted to household estimates using the average household size in each
state as captured by the 2018 DHS survey (see [dhs2018]). Rurality is defined
using an aggregation of the URCA definition (see [Cattaneo2021]). Each group
reflects a more rural region, starting with cities of all sizes which are
aggregated to the group ‘Urban core’, to very remote areas including those
further than 1 hour from a small city or town which are aggregated into the
final group ‘<1hr to small city/town+’. Electrification estimates are taken
from AtlasAI spatial electrification data for the year 2020 (see atlasai.com).
Our work focusses on grid-electrified households outside the urban core.

### Sampling

In total 3,600 households were intended to be surveyed, split evenly into the
three geopolitical zones. Households were sampled from 225 enumeration areas
(EAs) consisting of 16 households each. In addition, 5 firms were to be
surveyed in each enumeration area, giving a total sample of 1,125 firms.
Within each zone, half of the states (four in total per state) were selected
using systematic probability proportionate to size sampling (PPS) in order to
reduce survey costs. The count of households at the state level was used as
the weighting variable in the PPS sampling. This approximates a representative
zonal sample when not able to visit all states within each zone. The state-
level pre-sampling may introduce bias with respect to the rurality of sparsely
populated states not included in our sample. To address this the distribution
of households with respect to the URCA PeopleSuN categories across all states
in each zone is used to adjust subsequent sampling within selected states.
Table 4 describes the approximate proportion of grid-connected households in
each zone and category. This is done including all states, not just those
sampled. The proportions therefore represent the share of people living in
each category that should be replicated within the restricted sample of
states. This proportional stratification results in a sample size within each
zone and category that reflects, as far as possible, a representative sample
of grid-electrified rural and peri-urban households across the zone.

The final enumeration areas within each zone are selected using PPS sampling
once again, weighted by the total number of households in each cell to achieve
the sample size by zone as defined in the Desired EAs column. A total sample
of 225 enumeration areas is equivalent to a zonal sample of 75 enumeration
areas, distributed as per the proportional stratification across each of the
three zones. Within each zone, this results in a sample that is representative
of grid-electrified households outside the urban core while reducing the cost
of survey implementation. Analysis can therefore be conducted at the zonal
level as is, and re-weighted to arrive at a representative ‘national’ sample
across all three contiguous zones if desired. The necessary weights for
aggregate analyses across all three contiguous zones are provided with the
dataset.

### Implementation

Survey implementation was conducted by eHealth Africa (eHA), who also secured
state-government permission to conduct surveys in each state. The survey was
conducted using the Kobo Collect Computer-Assisted Personal Interviews (CAPI)
technology. All EAs within security compromised Local Government Areas (LGAs)
were identified and shared with the research team for replacement. From the
total of 255 EAs originally planned (reflecting a desired sample of 225 EAs
and 30 buffer EAs), 247 EAs remained during implementation. From the 21st to
the 23rd June 2021, a training of trainers was conducted by the research team
for eHA’s key staff and field supervisors who were scheduled to work on the
project. Following this, the translation of survey instruments into local
languages (Hausa and Pidgin) was finalized on the 12th of July. This was
followed by field testing which was conducted on the 13th and 14th of July
2021, and a field testing report was submitted on 16th July 2021. Step-down
training of Enumerators was simultaneously conducted between 25th - 29th July
2021 in 3 locations (Kano, Abuja, and Akwa Ibom) to accommodate the
enumerators by zone and reduce travel costs. Kano hosted the North West
states’ enumerators while Abuja and Akwa-Ibom hosted North Central and South-
South enumerators respectively with the report submitted on the 30th of July
2021. Pre- and post-training assessments were conducted and enumerators that
met the evaluation benchmarks were finally selected. A total of 60 Enumerators
and 12 State Supervisors were engaged in the quantitative survey activity.

Data collection commenced on August 7th. A team comprising 2 enumerators
(mostly paired male and female) worked to cover 16 households and 5
enterprises in each EA. Community leaders and local authorities were consulted
before enumerators’ visits and commencement of activity at the LGA and
community levels. The team faced challenges in gathering survey data due to
security, communal clashes and inaccessibility in certain EAs, requiring
replacement with a list of buffer enumerations areas within each zone. As a
result, there is some small level of bias with respect to the zones given the
mismatch in URCA group proportions, which is shown in Table 4. This remains
small overall and the mechanism underlying this transparently communicated
here. Furthermore, 1 enumeration area in North Central, and 6 in North West
were not grid-connected (despite being identified as electrified within the
AtlasAI data), but were using electricity in other forms such as diesel
generators or solar devices. One EA (AK12) contains a sample of 15, rather
than 16 households which was not able to be completed due to banditry on the
final day of enumeration, giving us a final sample of 3,599 households. Figure
3 depicts the full sample of enumeration areas with household counts shown as
labels. Points have been jittered up to 10km.

Figure 3: Visualisation of the spatial distribution of the household sample.
Geopolitical zones that were not sampled are shaded dark grey. Actual sample
locations have been jittered up to 10 kilometres and labels reflect the total
number of households sampled (numbers greater than 16 indicate a cluster of
enumeration areas). The basemap includes the spatial electrification coverage
for 2020 as estimated by AtlasAI, shown as yellow pixels. Any geographic
boundary inaccuracies are unintentional. All secondary data is from identical
sources as stated for Figure 2.

Zone | URCA PeopleSuN category | Households | Share | Desired EAs | Sampled EAs | Grid-connected
---|---|---|---|---|---|---
North Central | Town (0.02M) | 98297 | 0.11 | 8 | 9 | 9
North Central | 1hr to large city | 364067 | 0.4 | 30 | 30 | 30
North Central | 1hr to int. city | 301774 | 0.33 | 25 | 25 | 24
North Central | 1hr to small city/town+ | 147266 | 0.16 | 12 | 11 | 11
North West | Town (0.02M) | 243730 | 0.2 | 15 | 17 | 17
North West | 1hr to large city | 413041 | 0.35 | 26 | 27 | 26
North West | 1hr to int. city | 275368 | 0.23 | 17 | 18 | 18
North West | 1hr to small city/town+ | 261740 | 0.22 | 17 | 13 | 8
South South | Town (0.02M) | 188965 | 0.11 | 8 | 10 | 10
South South | 1hr to large city | 1012942 | 0.61 | 46 | 46 | 46
South South | 1hr to int. city | 356610 | 0.22 | 17 | 16 | 16
South South | 1hr to small city/town+ | 98675 | 0.06 | 5 | 3 | 3

Table 4: Desired and final enumeration areas per zone and URCA PeopleSuN
category. Households describes the approximate number of grid-connected
households in each category. Desired EAs describes the desired allocation of
enumeration areas providing a representative sample of rural and peri-urban
grid-connected households in each zone. Sampled EAs describes the actual final
sample following implementation and Grid-connected describes those that were
indeed grid-connected.

## Data Records

All data is publicly available here
https://dataverse.harvard.edu/dataset.xhtml?persistentId=doi:10.7910/DVN/GTNEJD

## Summary statistics

As this is a novel survey dataset with a transparent sampling design described
above, we provide here an overview of the collected data. This includes sample
characteristics, lighting sources used by households and enterprises, self-
reported supply quality, appliance ownership, cooking solutions, and energy
service satisfaction. Several additional variables exist in the dataset beyond
that which are shown here.

### Sample characteristics

Summary statistics of the household and enterprise samples are shown in Table
5 panels (a) and (b). These aggregates provide an indication of sampled
household and enterprise characteristics across common socio-economic,
demographic and asset-wealth variables.

Variable | North Central | North West | South South
---|---|---|---
Age of household head | 47.25 | 47.39 | 45.80
Household adults | 4.19 | 4.56 | 3.21
Number of bicycles | 0.14 | 0.43 | 0.23
Number of motorbikes | 0.62 | 0.61 | 0.42
Number of cars | 0.37 | 0.16 | 0.32
Share using Bank | 0.79 | 0.54 | 0.86
Share using Mobile Money | 0.24 | 0.04 | 0.27
Weekly (non-energy) expenditures | 10317.08 | 12643.75 | 13758.67

(a) Household

Variable | North Central | North West | South South
---|---|---|---
Age of owner | 37.21 | 34.53 | 39.11
Female owner | 0.66 | 0.09 | 0.59
Number of bicycles | 0.02 | 0.22 | 0.09
Number of motorbikes | 0.37 | 0.48 | 0.34
Number of cars | 0.15 | 0.06 | 0.15
Share using Bank | 0.62 | 0.45 | 0.71
Share using Mobile Money | 0.18 | 0.05 | 0.27
Total assets | 212658.71 | 207399.19 | 271132.80
Full-time employees | 1.62 | 1.38 | 1.52
Monthly salaries | 23735.14 | 26444.35 | 24694.40

(b) Enterprise

Table 5: Sample summary statistics across common socioeconomic, demographic
and asset-wealth characteristic variables. All monetary aggregates are
provided in Nigerian Naira (2021).

Table 6 provides sample aggregates of the rates of grid electrification at the
community-level, together with the household and enterprise access to
electricity. Notably, we find that over half of the households interviewed
also owned backup sources ranging from electric generators to rechargeable and
dry-cell batteries. We also note once more that despite efforts to establish a
sample of grid-electrified communities, our sample indeed includes a small
subset of communities that were not grid-electrified, but utilize other
sources of electricity, as described in the sampling design and implementation
section above.

Access | North Central | North West | South South
---|---|---|---
Community-level
National grid connection | 99% | 92% | 97%
Household-level
National grid connection | 98% | 85% | 93%
Local minigrid | 0% | 0% | 4%
Solar home system | 1% | 6% | 1%
Solar lantern | 1% | 2% | 0%
Electric generator | 37% | 18% | 66%
Rechargeable battery | 35% | 26% | 30%
Dry cell battery / torch | 59% | 68% | 32%
Other | 10% | 5% | 8%

Table 6: Aggregate community-, household-, and enterprise-level energy access
rates. Top: Share of enumeration areas that were in-fact grid connected (note:
as described in the research design, the intended sample of grid-electrified
communities included a minority of communities that were not grid-connected by
consumed electricity in some way). Bottom: Share of households and enterprises
using each possible source of lighting. The latter adds to more than 100% due
to fuel stacking discussed later. See Table 7 for further detail describing
patterns of stacking of each of the above solutions for the households and
enterprises.

### Household and enterprise electricity supply stacking

Table 7 describes typical primary, secondary and tertiary lighting sources
among almost (almost exclusively) grid-connected communities in rural and
peri-urban regions of each zone. As noted earlier, we find significant
‘electricity source stacking’ among the sampled households. Dry cell batteries
and electric generators are the dominant additional sources of lighting after
the national grid, with large differences in the penetration of these sources
across the three zones. It is notable that only about half of the households
are using the national grid as their primary source of lighting in two of the
three zones. Helping to explain this, Figure 4, shows self-reported average
daily grid electricity supply duration. Approximately 50% of both households
and enterprises in all zones reported an average less than 8 hours of
electricity per day delivered via national grid.

| Primary | Secondary | Tertiary
---|---|---|---
North Central
National grid connection | 77.2% | 17.2% | 4.0%
Electric generator | 7.3% | 26.1% | 3.4%
Rechargeable battery | 2.5% | 18.5% | 13.2%
Dry cell battery / torch | 12.3% | 26.6% | 14.8%
Other | 0.7% | 1.2% | 6.2%
North West
National grid connection | 46.8% | 29.6% | 8.3%
Electric generator | 6.9% | 8.9% | 2.2%
Rechargeable battery | 7.4% | 15.2% | 3.2%
Dry cell battery / torch | 32.3% | 28.0% | 5.6%
Other | 6.4% | 2.9% | 4.0%
South South
National grid connection | 40.9% | 42.5% | 10.1%
Electric generator | 39.4% | 23.1% | 3.3%
Rechargeable battery | 3.7% | 12.8% | 12.3%
Dry cell battery / torch | 10.7% | 9.2% | 7.3%
Other | 5.3% | 1.1% | 4.3%

(a) Households

| Primary | Secondary | Tertiary
---|---|---|---
North Central
National grid connection | 70.4% | 22.4% | 4.8%
Electric generator | 17.9% | 29.9% | 2.9%
Rechargeable battery | 2.7% | 14.7% | 8.5%
Dry cell battery / torch | 5.6% | 16.3% | 5.9%
Other | 3.5% | 2.9% | 1.3%
North West
National grid connection | 41.1% | 35.2% | 6.5%
Electric generator | 34.7% | 16.9% | 1.6%
Rechargeable battery | 5.1% | 10.5% | 3.8%
Dry cell battery / torch | 10.5% | 15.3% | 7.3%
Other | 8.6% | 3.8% | 2.2%
South South
National grid connection | 38.1% | 48.3% | 5.9%
Electric generator | 52.0% | 17.9% | 0.8%
Rechargeable battery | 1.9% | 11.5% | 8.8%
Dry cell battery / torch | 3.7% | 5.1% | 4.5%
Other | 4.3% | 0.8% | 1.1%

(b) Enterprises

Table 7: Descriptive summary of primary, secondary and tertiary lighting
sources at the zonal level. Households may rely on more than three distinct
sources of electricity and lighting in some cases, which we do not show here.
Figure 4: Distribution of self-reported daily grid electricity supply duration
on average over the past week. The red line shows the cumulative proportion of
the household sample that reported between zero and x (the value on the
horizontal axis) hours of grid connection supply time per 24 hours. The blue
line shows the same for enterprises.

### Household cookstove stacking

Cooking with ‘unclean cooking fuels’ is widespread throughout the sampled
zones333Electric cooking, LPG and biogas are classified as ”clean cooking”
while kerosene and Solid-fuel are among ‘unclean cooking fuels’
[Household_air_pollution2022].. The majority of sampled North West households
rely primarily on biomass for cooking, with a significant gap between this and
LPG. The highest rates of clean cooking are found among South South households
with two thirds relying primarily on LPG. Despite grid connection and access
to electricity, electric-cooking is not widespread among the sampled
households.

| Primary | Secondary
---|---|---
North Central
Electric | 6.5% | 12.2%
LPG | 38.5% | 29.8%
Biogas | 2.3% | 1.0%
Kerosene | 9.3% | 29.3%
Biomass | 43.5% | 27.8%
North West
Electric | 0.5% | 7.5%
LPG | 8.4% | 21.8%
Biogas | 4.8% | 13.5%
Kerosene | 2.5% | 21.1%
Biomass | 83.8% | 36.1%
South South
Electric | 1.5% | 15.2%
LPG | 65.0% | 25.1%
Biogas | 1.7% | 0.5%
Kerosene | 18.5% | 39.8%
Biomass | 13.4% | 19.4%

Table 8: Summary of household cookstove stacking. Stove use is disaggregated
by primary and secondary use which is based on the stove used most with
respected to the reported minutes it is operated over a typical day.

### Appliance ownership

Figure 5 describes the electrical appliance ownership among sampled households
and enterprises across the three zones. This figure provides an introduction
to the different types of appliances for which ownership and use data were
collected. The list of selected appliances queried draws from stakeholder
discussions and appliance ownership survey modules in the Multi-Tier Framework
for Measuring Energy Access surveys[Bhatia2015]. These ownership patterns
reveal the heterogeneity in energy service use among grid-connected households
by zone.

Figure 5: Appliance ownership rates for each appliance. Selected aggregate
electrical appliance ownership rates for households (red) and enterprises
(blue).

### Energy service satisfaction

Finally, ten different energy service satisfaction questions are presented in
Figures 6 & 7. Despite having the largest share of households that own fans
and air conditioners, almost two-thirds of South South households believe they
are failing to maintain their homes at a comfortable temperature. Regarding
their ability to retain perishable food items for extended periods of time and
their access to appropriate light when necessary, sampled households across
all zones expressed a high level of discontent. North West with the lowest
share of phones and televisions in the appliance list, shows the lowest
satisfaction with access to communication devices and digital information and
entertainment. With regards to enterprises, there is a significant perceived
gap between the current energy service levels and what enterprises require for
growth. Approximately two-thirds of all surveyed enterprises across all zones
were nearly equally unsatisfied with each of the aspects we inquired upon.
Heterogeneity across industry, location and size deserves further attention
here in subsequent analyses.

Figure 6: Reported household energy service satisfaction. Answers to six
questions regarding the electricity service satisfaction. Figure 7: Reported
enterprise energy service satisfaction. Answers to four questions regarding
the electricity service satisfaction.

## Usage Notes

We present here a novel survey dataset describing demographic and
socioeconomic characteristics, electricity access and supply quality,
electrical appliance ownership and usage time, cooking solutions,
capabilities, and preferences across households and enterprises living in
grid-electrified communities outside the urban core in Nigeria. The dataset is
provided as a set of .csv and .xlsx files. Table 9 describes each file.

Filename | Description | Usage
---|---|---
peoplesun_hh_odk_codebook.xlsx | Household questionnaire codebook | Link to household dataset by variable name
peoplesun_hh_odk_choices.xlsx | Household multi- or single-select question labels | Link to codebook by list name
peoplesun_hh_anon.csv | Anonymised household survey dataset | Apply weights when aggregating zones
peoplesun_hhstoves_anon.csv | Household stove roster | Link to household dataset by household ID
peoplesun_hhapps_anon.csv | Household appliance roster | Link to household dataset by household ID
peoplesun_ent_odk_codebook.xlsx | Enterprise questionnaire codebook | Link to enterprise dataset by variable name
peoplesun_ent_odk_choices.xlsx | Enterprise multi- or single-select question labels | Link to codebook by list name
peoplesun_ent_anon.csv | Anonymised enterprise survey dataset | Apply weights when aggregating zones
peoplesun_entstoves_anon.csv | Enterprise stove roster | Link to enterprise dataset by enterprise ID
peoplesun_entapps_anon.csv | Enterprise appliance roster | Link to enterprise dataset by enterprise ID
peoplesun_entequips_anon.csv | Enterprise equipment roster | Link to enterprise dataset by enterprise ID
eaidgeokey_anon.csv | Basic geospatial data per enumeration area | Link to datasets by enumeration area ID

Table 9: Overview of files included in the presented dataset.

This dataset can inform decision makers, project developers and researchers
about the current energy supply situation and model future energy use towards
securing a decent living standard for all in rural and peri-urban areas of
Nigeria. Our preliminary assessment points to three key areas of further
research using this data. First, we propose modelling appliance ownership
likelihoods, electricity consumption levels and energy service needs in un-
electrified regions. Surveyed use of appliances among grid-connected
communities can serve as a proxy to estimate future electricity demand for
similar households which are currently not grid connected. We find the
appliances used are mainly light bulbs, phone charger, fans, television and
kettles / irons and fridges (with a higher variance across the zones).
Appliance ownership is combined with detailed data describing patterns of
acquisition and use, aiding modelling efforts. Second, we propose identifying
supply-side and demand-side solutions to address high usage of diesel
generators. The collected data provides evidence of very unreliable grid
supply among rural and peri-urban grid-electrified communities. In all zones,
approximately 50% of households and enterprises reported receiving less than 8
hours of electricity per day from the national grid. A significant proportion
of enterprises and households cope with this situation by using diesel
generators as primary or secondary lighting source. Thirdly, we propose
exploring broader issues of multi-dimensional energy access, access to decent
living standards and climate vulnerability. The data indicates that households
in all zones generally expressed high levels of dissatisfaction regarding
their to access basic energy services such as keeping oneself at a comfortable
temperature, though heterogeneity across the zones was evident. With regard to
productive uses of electricity, roughly 60% of all surveyed enterprises are
dissatisfied with energy supply affordability, reliability and adequacy,
alongside their access to necessary appliances.

A common theme across these three research areas is the importance of
increasing the central grid supply quality and in providing cost-competitive
decentralized alternatives to improve living conditions and business
perspectives in Nigeria. Although our research focusses on energy access, we
understand that achieving SDG7 - providing sustainable energy for all - is not
the silver bullet for increasing living conditions and business perspectives
on its own. A myriad of accompanying factors such as access to other basic
services including education, sanitation and health, access to capital and
broader issues of gender equality must be addressed. Nevertheless, SDG7 is a
key enabling achievement as many other SDGs depend upon sustainable energy
access. With this in mind, it is clear that achieving improving energy access
in Nigeria is both about new connections and about increasing the supply
quality in weak grid areas. The existing grid network needs to be strengthened
and decentralised renewables will play a role in both peri-urban and rural
regions. Furthermore, in order to satisfy energy service needs in the context
of a changing global climate, demand-side policies supporting household
acquisition of appliances necessary for a decent living standard must be
explored. A cross-cutting analysis combining expertise from institutions
responsible for centralised network planning and those responsible for
decentralised electrification efforts would be ideal to strategically plan
these efforts. We hope that the data we present can be useful in providing
evidence for such an analysis.

## Code availability

As we are presenting collected survey data, no custom code was used or is
necessary to generate or work with this data. Complete codebooks describing
the questionnaires are available here:
https://dataverse.harvard.edu/dataset.xhtml?persistentId=doi:10.7910/DVN/GTNEJD.

## Acknowledgements

This research work is part of the three-year research and development project
"People Power: Optimizing off-grid electricity supply systems in Nigeria"
(PeopleSuN). PeopleSuN is a project funded by the German Federal Ministry of
Education and Research (BMBF) within the funding initiative "Client II -
International Partnerships for Sustainable Innovations" (FKZ 03SF0606A). We
thank eHealth Africa for their impeccable work in collecting the data and
their cooperation. We would also like to thank Tomiwa Aileru, Clara Bruegge,
Oriyomi Amusa and Ulrike Lich who supported us in the preparation of the
survey instruments and processing of the data.

## Author contributions statement

S.P., C.B, and P.B. conducted stakeholder discussions to define the data gap
(for details, see https://www.peoplesun.org/). S.P. developed the sampling and
data collection methods. eHealthAfrica implemented the data collection. S.P.
conducted the analysis presented here. All authors contributed in drafting the
accompanying text.

## Competing interests

The authors declare no competing interests.

## Figures & Tables


# Observation of room temperature gate tunable quantum confinement effect in
photodoped junctionless MOSFET

Biswajit Khan1, Abir Mukherjee1, Yordan M. Georgiev2, J.P. Colinge3, Suprovat
Ghosh1 and Samaresh Das1<EMAIL_ADDRESS>1Centre for Applied
Research in Electronics, Indian Institute of Technology Delhi, New Delhi
110016, India.

2Institute of Ion Beam Physics and Materials Research, Helmholtz-Zentrum
Dresden-Rossendorf(HZDR), Dresden01328, Germany.

3Materials Chemistry and Analysis Group, Department of Chemistry and Tyndall
National Institute, University College Cork, Lee Maltings, Dyke Parade,
Western road, Cork, Ireland.

###### Abstract

In the pursuit of room temperature quantum hardware, our study introduces a
gate voltage tunable quantum wire within a tri-gated n-type junctionless
MOSFET. The application of gate voltage alters the parabolic potential well of
the tri-gated junctionless MOSFET, enabling modification of the nanowire’s
potential well profile. In the presence of light, photogenerated electrons
accumulate at the center of the junctionless nanowire, aligning with the
modified potential well profile influenced by gate bias. These carriers at the
center are far from interfaces and experience less interfacial noise.
Therefore, such clean photo-doping shows clear, repeatable peaks in current
for specific gate biases compared to the dark condition, considering different
operating drain-to-source voltages at room temperature. We propose that
photodoping-induced subband occupation of gate tunable potential well of the
nanowire is the underlying phenomenon responsible for this kind of
observation. This study reveals experimental findings demonstrating gate-
induced switching from semi-classical to the quantum domain, followed by the
optical occupancy of electronic sub-bands at room temperature. We developed a
compact model based on the Nonequilibrium Green’s function formalism to
understand this phenomenon in our illuminated device better. This work reveals
the survival of the quantum confinement effect at room temperature in such
semi-classical transport.

##  I Introduction

The quantum confinement effect (QCE) is an extraordinary phenomenon in solid-
state devices at cryogenic temperatures Elzerman et al. (2004); Xia and Cheah
(1997); Petta et al. (2005); Angus et al. (2007); Rustagi et al. (2007); Li et
al. (2013). QCE manifests when the device dimensions are on the same order of
magnitude as the De Broglie wavelength of the carriers (electrons or holes),
resulting in the quantization of energy levels. At ultra-low temperatures, the
energy gaps between these discrete levels are significantly more significant
than thermal fluctuations, enabling the distinct identification of these
quantized energy levels in experiments.However, at room temperature (RT), the
thermal energy vastly exceeds the energy differences between these quantized
energy levels, making it highly challenging to observe QCE. Nevertheless,
achieving QCE at RT is crucial for advancing quantum electronics hardware. To
accomplish this, devices must be extremely thin. Fabricating such nanodevices
with traditional junctions is a complex process, requiring precise doping
profiles at sub-nanometer junction regions. In this context, junction-less
transistors Colinge et al. (2011); Lee et al. (2009); Amin and Sarin (2013);
Colinge et al. (2006a); Das et al. (2016) have gained significant attention
due to their practical advantages.In the past few years, Silicon quantum dot
(QD) with nanometer islands Gorman et al. (2005); Yang et al. (2016) and
junctionless nanowire (Si NW) Nishiguchi et al. (2006); Shi et al. (2013);
Shaji et al. (2008); MacQuarrie et al. (2020); Schoenfield et al. (2017);
Penthorn et al. (2019); Hu et al. (2007) based QDs show massive potential for
applications in quantum computation Elzerman et al. (2004); Petta et al.
(2005); Veldhorst et al. (2015), quantum sensing Degen et al. (2017); Elzerman
et al. (2004); Gonzalez-Zalba et al. (2016); Berman et al. (1997); Zhang et
al. (2005), and provide a platform for CMOS-compatible nanoelectronics Nikonov
and Young (2013).QCE at RT in 5 nm P-silicon [110] NW on Silicon-on-insulator
(SOI) substrates by top-down approach is demonstrated experimentally Trivedi
et al. (2011); Yi et al. (2011). Buin et al. (2008); Singh et al. (2008)
showed enhanced performance based on RT QCE-based Si NW on SOI. SOI-based
n-Si(110) NW field-effect transistor (FET) shows RT QCE, and SOI-based
n-Si(100) NW FET shows RT single electron/hole transport Suzuki et al. (2013).
When incident light interacts with the phototransistor, photogenerated
electrons and holes disperse into the channel region by the band profile, thus
modifying the carrier distribution. This, in turn, leads to a modulation in
conductivity, a phenomenon known as photogating Konstantatos et al. (2012);
Jiang et al. (2022); Yuan et al. (2018); Huang et al. (2016); Guo et al.
(2016).For Si NW-based transistors, the $Si/SiO_{2}$ interface provides a
self-assembled electric field due to band bending at the interface, and this
field accumulates photogenerated charges depending on the band bending; these
are called photoinduced trap charges. These trap charges act as a photogate
voltage, altering the carrier profile and, ultimately, the channel’s
conductance. Introducing carriers into the channel region via a photogate
voltage termed photo doping of the channel. Photoinduced doping is an enhanced
form of doping in materials such as semiconductors under the influence of
light Aftab et al. (2022); Wu et al. (2017); Yan et al. (2009); Calarco et al.
(2005). Photo-generation and efficient separation of charge carriers under the
influence of an inner electric field in $p-n$ heterojunctions have produced a
photon-modulated convertibility between negative differential resistance and
resistive switching in such systems Zheng et al. (2019).

##  II Results

In this study, we investigate the behavior of room-temperature Quantum
Confinement Effect (QCE)-based transport in the presence of light. As
illustrated in Figure 1a, we present a schematic representation of the tri-
gated junctionless transistor we fabricated. The dimensions of the silicon
nanowire (NW) are $5\,\mathrm{nm}\times 35\,\mathrm{nm}$ ($W_{\text{si}}\times
t_{\text{si}}$), and the gate length is $1\,\mu\mathrm{m}$. In contrast,
Figure 1b displays a scanning electron microscopic (SEM) image of the actual
device, and Figure 1c is a cross-sectional tunneling electron microscope (TEM)
image of the device.

Figure  1: Device structure and electron microscopic images. (a) Schematic of
the device structure, (b) Typical scanning electron microscopic (SEM) image of
the device, (c) Cross-sectional Tunneling electron microscope (TEM) image of
the device.

Figure 2 presents the results of photocurrent measurements for our
junctionless transistor under different drain biases, specifically, at 50 mV
and 300 mV when exposed to 700 nm light at room temperature (RT). Notably, a
significant increase in current is observed under illumination compared to the
dark current. Of particular interest is the presence of oscillations in the
drain current concerning variations in gate voltage under illumination. To
further investigate the influence of gate voltage on the drain current over a
range of drain biases, we systematically varied the drain bias from -200 mV to
200 mV and recorded the transfer characteristics. This comprehensive
examination is illustrated in the plane of drain and gate biases, as shown in
Figure 2c. The graph’s white lines delineate the drain bias values range where
we observed significant gate tunability. Figure 2b reveals substantial gate
tunability in the photocurrent from $V_{\text{DS}}=+10\,\mathrm{mV}$ to
$V_{\text{DS}}=+60\,\mathrm{mV}$ at RT. For higher drain bias, this tunability
vanished Colinge et al. (2006a); Colinge (2007); Colinge et al. (2006b);
Rustagi et al. (2007); Lee et al. (2010); Trivedi et al. (2011); Ma et al.
(2015); Li et al. (2013); Je et al. (2000), which is further discussed in
Section B of Supplementary Note (SN)-4 in Supplementary Information (SI) NAT
(2024). To see those peaks more prominently, we plotted the transconductance
with respect to gate voltage for drain voltage 20 mV and 50 mV, respectively,
in Figure 2d. Transconductance behavior for other drain voltages is
illustrated in Section A of SN-4 in SI NAT (2024). In addition to that,
channel conductance behavior with respect to gate voltage is also illustrated
in Section B of SN-4 in SI NAT (2024).

Figure  2:  Photocurrent measurements. (a) Transfer characteristics under dark
(black) and illumination (red) conditions at $V_{DS}=50mV$ and illuminated
condition(blue) at $V_{DS}=300mV$.(b) Transfer characteristics under
illumination conditions for $V_{DS}=+10mV$ to $60mV$ (c) Contour plot: Drain
current in the bias plane of drain voltage and gate voltage (d)
Transconductance vs gate voltage plot at $V_{DS}=+10mV$(black) and
+$50mV$(red) respectively. Figure  3: Cross-sectional schematic and energy
band diagrams.  (a) Cross-sectional schematic of the device, where AB cut line
is along $t_{si}$(y-axis), and CD cut line is along $W_{si}$(x-axis). (b)
Schematic of the Si channel with characteristic
directions:$t_{si}$(y-axis),$W_{si}$(x-axis) and channel, $L$(z-axis). The
asymmetry in the band diagram is due to the single gate configuration along
this cut line AB. $E_{FP}$(0)=$E_{FM}$(0) is Fermi level of $P^{+}$
polysilicon gate when the gate voltage is zero. The solid black line is for
dark, and the dashed red line is for the illuminated conditions, respectively,
when (c) Gate voltage $V_{GS}$=0 and (d) Gate voltage $V_{GS}$ is negative.
The symmetry in the band diagram is due to the double gate configuration along
this cut-line CD. Energy band diagram along the cut line CD: The solid black
line is for dark, and the dashed red line is for the illuminated conditions,
respectively, when (e) Gate voltage $V_{GS}$=0 and (f) Gate voltage $V_{GS}$
is negative.

In the presence of light, an electron-hole pair (e-h pair) will be generated,
and the electrons will be accumulated in the core of the channel due to band
confinement, which is shown pictorially in figure-3c to 3f. To understand this
electron accumulation at the center of NW more clearly, we illustrated the
energy band diagrams along the cutline, denoted as the red dotted lines,
namely AB and CD, respectively, in figure-3a. As the photo-generated hole
moves toward the surface due to upward band bending (negative surface
potential), consequently, the photo-generated electrons move toward the core
of the nanowire (figure-3c to 3f). Due to this spatial quarantine of photo-
generated e-h pairs, an additional positive gate voltage is induced by
accumulated holes at the interface of $Si/Sio_{2}$ of the NW channel, which in
turn modulates the concentration of electrons at the core of n channel NW. In
other words, in addition to the applied gate voltage, this also modulates the
channel’s conductance. Electrons are doping the core of the nanowire (NW) due
to light and potential well modification of NW due to gate bias, and we term
this phenomenon the photo doping of electrons in the NW core. Upon the
application of a negative gate voltage to the top gate, there will be more
accumulation of holes at the interface of the $Si/Sio_{2}$(figure-3d and 3f).
This can be understood in this way: when the negative gate voltage is applied
to the top gate, the slope of the energy band near the interface will be
stiffened compared to the no gate voltage case (figure-3c and 3e) to support
the high electric field. Due to this, the availability of states near the
interface will increase. Therefore, for a high electric field, more photo-
generated holes will be translated toward the $Si/Sio_{2}$ interface and
facilitated into the increased states at the interface. Consequently, more
electrons will be accumulated at the center of the channel due to the increase
in efficiency in e-h pair separation and the rise in positive photo gate
voltage. This electron accumulation at the center of NW is illustrated by the
light shadow of pink color in figure-3d and 3f. Therefore, negative gate
voltage increases the holes at the interface, and as a consequence of the
increase in induced positive photo gate voltage, the concentration of
electrons at the center eventually increases. In other words, band bending
near the interface will increase as we increase the negative gate voltage,
introducing a high recombination barrier for photo-generated e-h pairs. Our
NEGF modeling effectively captures this event, as illustrated in figure-S3 of
SI NAT (2024). Therefore, the efficiency of separating the photo-generated e-h
pair will increase, and eventually, the concentration of electrons at the
center will increase, too. We are calling it enhanced photo doping of the
channel.

##  III Discussion

A channel of photo-generated electrons is induced at the center of the
nanowire due to photo-doping effects resulting from modification of the
potential well of NW due to gate voltage. This occurs through the bulk
conduction mechanism of the junctionless transistor(JLT) Colinge et al. (2011,
2010); Trevisoli et al. (2012); Rai et al. (2022); Colinge (1990, 2012); Gnani
et al. (2011), which is explained in SN-2 section of SI NAT (2024). To
extensively clarify the photo doping in our device, we modeled device behavior
using the Nonequilibrium Green’s Function Method (NEGF). The modeling starts
with constructing a closed system Hamiltonian, and then we build the electron-
photon interaction term, namely, the electron in scattering function. This
function accounts for the excess electrons entering the system due to
electron-photon interaction. Ultimately, we add this to the closed system
Hamiltonian to make it an open system. After that, we perform Schrödinger-
Poison’s self-consistency to find the modified Hamiltonian and consequently
system Green’s function, local density of state (LDOS) in dark and
illumination conditions, and carrier distribution. The motion of charges in
the quantum system is described by the Schrödinger-Poisson equations in the
following manner:

$\displaystyle\hat{H}\ket{\psi_{n}}$ $\displaystyle=E_{n}\ket{\psi_{n}}$ (1)
$\displaystyle\partial_{l}\epsilon_{lm}\partial_{m}\Phi(x_{k})$
$\displaystyle=-(\pm e\rho(x_{k}))$ (2)
$H^{(C/V)}_{iso}=-\frac{\hbar^{2}}{2m_{0}}\sum_{l,m}(\partial_{l}{(m{{}^{*}}_{lm})^{-1}}\partial_{m})+\phi^{(C/V)}(x,y)$
(3)

where $x_{k}\equiv(x_{1},x_{2},x_{3})$ in Cartesian coordinates. $\pm$ depicts
the charge polarity of the hole and electron, respectively. The complete
Hamiltonian for this system can be expressed in the following manner :

$\begin{split}H=&\sum_{i}H^{c}_{iso}c^{{\dagger}}_{i}c_{i}+\sum_{j}H^{v}_{iso}v^{{\dagger}}_{j}v_{j}+\sum_{\beta}\hbar\omega_{\beta}b^{{\dagger}}_{\beta}b_{\beta}\\\
&+\sum_{k}H^{G}_{iso}c^{{\dagger}}_{K}c_{K}+\sum_{i,j,\beta}(\tau^{\beta}_{ij}c^{{\dagger}}_{i}v_{j}b_{\beta}+\tau^{\beta\star}_{ji}c^{{\dagger}}_{i}v_{j}b^{{\dagger}}_{\beta}\\\
&+\tau^{\beta}_{ij}v^{{\dagger}}_{j}c_{i}b_{\beta}+\tau^{\beta\star}_{ji}v^{{\dagger}}_{j}c_{i}b^{{\dagger}}_{\beta})\end{split}$
(4)

where $(l,m)$ represent $(x,y,z)$. $\phi^{(C/V)}(x,y)$ is the distribution of
potential energy of the conduction/valance band in $x$ and $y$ directions.
$\epsilon_{lm}$ and $(m^{*})_{lm}$ are the permittivity tensor and effective
mass tensor, respectively, whose components vary at interfaces (e.g.,
semiconductor-insulator and metal-insulator). Pursuing the Heisenberg equation
of motion, equation-4 results in the pertinent equations governing the motion
of photo-generated electrons, incident photons, and holes:

$\begin{split}i\hbar\frac{d}{dt}c_{i}&=H^{C}_{iso}c_{i}+\sum_{j,\beta}\left(\tau^{\beta}_{ij}v_{j}b_{\beta}+\tau^{\beta\star}_{ij}v_{j}b^{{\dagger}}_{\beta}\right)+\sum_{r}\zeta_{ik}c_{k}\\\
i\hbar\frac{d}{dt}c_{k}&=H^{G}_{R}c_{k}+\sum_{i}\zeta^{\star}_{ki}c_{i}+\sum_{j}\zeta^{\star}_{kj}v_{j}\\\
i\hbar\frac{d}{dt}b_{\beta}&=E_{\beta}b_{\beta}+\sum_{i,j}\left(\tau^{\beta\star}_{ij}c^{{\dagger}}_{i}v_{j}+\tau^{\beta\star}_{ji}v^{{\dagger}}_{j}c_{i}\right)\\\
i\hbar\frac{d}{dt}v_{j}&=H^{V}_{iso}v_{j}+\sum_{j,\beta}\left(\tau^{\beta}_{ij}c_{i}b_{\beta}+\tau^{\beta\star}_{ij}c_{i}b^{{\dagger}}_{\beta}\right)+\sum_{r}\zeta_{jk}c_{k}\end{split}$
(5)

Here, $c_{i}$, $v_{j}$, and $c_{k}$ denote electron annihilation operators
corresponding to the $i^{th}$ state of the conduction band, $j^{th}$ state of
the valence band of the fin body, and $k^{th}$ state of the reservoirs (gate),
respectively. Additionally, $b_{\beta}$ is the annihilation operator for
photons at the $\beta$ mode. The term $\zeta_{ik}$ represents the interaction
between reservoirs and the active device (conduction band). It is worth noting
that the operator’s b’s, c’s, and v’s adhere to the Bose-Einstein (BE)
commutation and Fermi-Dirac (FD) anti-commutation relations,
$[b_{\beta},b^{+}_{\beta^{\prime}}]=\delta_{\beta\beta^{\prime}},\\{c_{i},c^{+}_{i^{\prime}}\\}=\delta_{ii^{\prime}},\\{v_{j},v^{+}_{j^{\prime}}\\}=\delta_{jj^{\prime}}$.
The Keldysh formalism is now employed to evaluate the two-time correlation
functions Keldysh et al. (1965).

Figure  4:  LDOS and carrier density under dark and light conditions from our
developed NEGF Model.  Local density of state (LDOS) along $W_{si}$: By
incorporating the eigenvalues from $t_{si}$(y-space) and L(z-space), we
reached an effective 3D Hamiltonian to get this coupled LDOS, (a) LDOS at dark
condition (b) Photo LDOS(PLDOS) at illumination condition at $V_{GS}=-0.64V$.
(c) LDOS at dark condition (d) Photo LDOS(PLDOS) at illumination condition at
$V_{GS}=-0.26V$. Electron density contour plot in the cross-section plane of
the nanowire(in XY plane or in $t_{si}\times W_{si}$ plane)(e) Without
illumination (f) With the illumination of 700 nm laser at $V_{GS}=-0.64$V and
(g) Without illumination (h) With the illumination of 700 nm laser at
$V_{GS}=-0.26$V respectively.

$\displaystyle n^{c}_{im}(t,t^{{}^{\prime}})$ $\displaystyle=$
$\displaystyle\langle
c^{{\dagger}}_{m}(t^{{}^{\prime}})c_{i}(t)\rangle,n^{v}_{jn}(t,t^{{}^{\prime}})=\langle
v^{{\dagger}}_{n}(t^{{}^{\prime}})v_{j}(t)\rangle$ (6) $\displaystyle
N^{ab\text{-}pht}_{\beta\beta^{{}^{\prime}}}(t,t^{{}^{\prime}})$
$\displaystyle=$ $\displaystyle\langle
b^{ph{\dagger}}_{\beta^{{}^{\prime}}}(t^{{}^{\prime}})b^{ph}_{\beta}(t)\rangle$

$n^{c}_{ij}$, $n^{v}_{jn}$, and $N^{ab-pht}_{\beta\beta^{{}^{\prime}}}$ denote
two-time correlation functions corresponding to the occupied state of
electrons in the conduction band and valence band, as well as the absorbed
photon, respectively. The electron and hole-in-scattering functions can be
written as belowAeberhard and Morf (2008); Mera et al. (2016); Aeberhard
(2012); Bertazzi et al. (2020); Kolek (2019); Sikdar et al. (2021, 2017)and
which is derived in theoretical modeling section.

$\begin{split}[\Sigma_{\text{elec}}(t,t^{\prime})]_{rs}&=\sum_{(l,k,\beta,\beta^{\prime})}\left\\{\tau^{\beta}_{rl}\left[N^{ab\text{-pht}}_{\beta\beta^{\prime}}(t,t^{\prime})n^{v}_{lk}(t,t^{\prime})\right]_{\text{iso}}\tau^{\beta^{\prime}\star}_{ks}\right\\}\\\
[\Sigma_{\text{hole}}(t,t^{\prime})]_{pq}&=\sum_{(l^{\prime},k^{\prime},\beta,\beta^{\prime})}\left\\{\tau^{\beta}_{pl^{\prime}}\left[N^{ab\text{-pht}}_{\beta\beta^{\prime}}(t,t^{\prime})n^{c}_{l^{\prime}k^{\prime}}(t,t^{\prime})\right]_{\text{iso}}\tau^{\beta^{\prime}\star}_{k^{\prime}q}\right\\}\end{split}$
(7)

Figure  5: Current under dark and light conditions from our theoretical model.
Calculated current from the charge density based on our theoretical model (a)
without illumination (b) With illumination at $V_{DS}=50mV$.

After the addition of reservoirs and in scattering function for electrons and
holes, the modified green’s function can be written asDatta (2000, 2005)

$\begin{split}G^{M}_{elec}(E)=([E_{elec}I]-[{H_{C}}^{iso}]-[\Sigma_{elec}(E)]-[\Sigma^{R}(E)]])^{-1}\\\
G^{M}_{hole}(E)=([E_{hole}I]-[{H_{V}}^{iso}]-[\Sigma_{hole}(E)]-[\Sigma^{R}(E)]])^{-1}\end{split}$
(8)

Hence, the concentration of photo-generated electron and hole concentration
per second is calculated as followsSikdar et al. (2021)

$\begin{split}p^{ph}=[{\int_{-\infty}^{E_{V}}}{[G^{M}_{hole}(E)][\Sigma_{hole}(E)][G^{M}_{hole}(E)]^{{\dagger}}}dE]\\\
n^{ph}=[{\int_{E_{C}}^{+\infty}}{[G^{M}_{elec}(E)][\Sigma_{elec}(E)][G^{M}_{elec}(E)]^{{\dagger}}}dE]\end{split}$
(9)

The transverse electronic state, n(x,y) can be split up into two transverse
subspace as followsSikdar et al. (2019)

$|n^{\tau}\rangle=|n_{x}\rangle\otimes|n_{y}\rangle$ (10)

The 2D carrier distribution in the fin body cross-section (i.e., x-y plane) is
given by,

$n_{2D}(x,y)=\sum_{n_{\tau}}f(E_{c}+E_{n_{\tau}}-E_{f})\langle{{n_{\tau}}}|{x,y\rangle}{\langle{x,y}|{n_{\tau}}\rangle}$
(11)

Then the charge concentration $(1/cm^{3})$ $\rho^{s}=\frac{qn_{2D}}{L_{z}}$ at
the source side and it was calculated followed by equation-11 and incorporated
into equation-2 to obtain the electrostatic potential and then compared with
the previous potential in the iteration loop to find the non-trivial
difference self consistently(as shown in figure-6 of manuscript). Therefore,
the charge concentration at the drain side for long-channel devices can be
calculated by Trevisoli et al. (2012)

$\begin{split}Q^{D}=Q^{S}-(V_{FB}-V_{G}+\Phi_{s})C_{ox}\end{split}$ (12)

where $Q^{S}=\rho^{s}W^{{}^{\prime}}H^{{}^{\prime}}$.Therefore the surface
potential-based drain current analytical expression is given byTrevisoli et
al. (2012),

$I_{D}={\frac{\mu}{L_{z}}}\biggr{[}{\frac{Q^{2}_{S}-Q^{2}_{D}}{2C_{ox}}}\biggl{]}$
(13)

Using equation-12 and 13, we have calculated $I_{D}$ and plotted it against
the gate voltage $V_{GS}$ for $V_{DS}$=50 mV for non illuminated case
(figure-5a) and illuminated case (figure-5b) respectively. From those two
figures, we can see that $I_{D}$ for dark is almost flat due to the deficiency
of electrons and the unavailability of states. This is illustrated in
figure-4a and 4e for peak A, corresponding to the $V_{GS}$=-0.64V. It is also
further illustrated for $V_{GS}$=-0.26V corresponding to peaks B, in figure-4c
and figure-4g, respectively. In section SN-5 of SI NAT (2024) corresponding to
peaks C is demonstrated. $I_{D}$ from our theoretical calculations for
illuminated case shows nearly exact peaks, namely A, B, and C, respectively,
for $V_{DS}$=50 mV (Figure-5b). These peaks approximately match our
experimental results (figure-2a). Here, the Gate voltage is pushing the device
to the quantum regime along $W_{si}$ direction, and being already in the
quantum domain in $t_{si}(=5nm)$ direction, we finally reach voltage tunable
quantum wire. A coupled mode LDOS of that quantum wire from our NEGF model is
illustrated in figure-4a and figure-4b for the non-illuminated and illuminated
case respectively for gate Voltage $V_{GS}$=-0.64V. This illustration
demonstrates the energy difference between the initial and subsequent sub-
bands is 50 meV for gate Voltage $V_{GS}$=-0.64V. Similarly, the energy
spacing of the first and third sub-band is 90 meV for gate Voltage
$V_{GS}$=-0.26V, which is shown in figure-4c and figure-4d respectively. The
energy spacing of the first and third sub-band is 90 meV for gate Voltage
$V_{GS}$=0.28V, which is illustrated in SN-5 of SI NAT (2024). These peaks A,
B, and C are related optically populated subbands for different gate voltage
$V_{GS}$=-0.64V,-0.26V, and 0.28V, respectively, at room temperature.
Theoretically, to see clear oscillation of current due to subband population
at room temperature, the subband energy separation must be equal to at least
3.5 times of thermal broadening energy ($K_{B}T$) Beenakker (1991); Heinzel
(2008); Yi et al. (2011), which is 91 meV. From our experiment and theoretical
model, the energy spacing of the first and second subband is approximately
1.96 (for 50 meV subband spacing for $V_{GS}$=-0.64V) times of thermal
broadening energy for peak A. For peaks B and C, the energy spacing of the
first and third subbands is approximately 3.5 times the thermal broadening
energy. Therefore, the creation of such sub-bands at room temperature and
corresponding distinct optical populations due to gate tunable quantum well is
justified as far as our device and its environment are concerned.

##  IV Conclusion

In summary, we conducted an experimental study on gate-tunable semi-classical
transport in a silicon nanowire top-gated junctionless n-channel MOSFET,
focusing on the quantum confinement effect when exposed to light at room
temperature. We verified this gate tunability of semi-classical transport by
measuring drain current while sweeping the gate voltage at various drain-to-
source bias settings. Notably, this gate tunability of QCE and corresponding
quantum mechanically controlled diffusive transport persisted under slight
drain-to-source bias but became less discernible at higher drain-to-source
bias levels. Additionally, we developed a compact model for light-electron
interactions within our nanostructure device and its corresponding transport
characteristics. This model helped us gain insights into the origins of the
observed anomalies in transport behavior at room temperature, utilizing the
nonequilibrium Green’s function (NEGF) approach. Therefore, this letter
unveils an abnormality in quantum mechanically controlled diffusive transport
that persists even at room temperature under the influence of illumination.
This will open up new possibilities for quantum devices at room temperature by
photo-doping in low-dimensional field-effect transistors.

##  V Methods

Theoretical Modeling: To understand the light-matter interaction, we need to
find the corresponding interaction matrix, which has been conceptualized as a
scattering picture as follows

$\tau=\langle f|\hat{V}_{e-\gamma}|i\rangle$ (14)

The state preceding the scattering event depicts the virtual electron
possessing the effective mass of the hole and momentum that is equal but
opposite., i.e., $\ket{i}=\ket{-\vec{k}^{hole}_{i},m^{\star}_{h}}$ and the
final state (post-scattering) of the real electron,
$\ket{f}=\ket{\vec{k}^{electron}_{f},m^{\star}_{e}}$ as shown by Feynman
diagram in figure-6a.

The carrier-photon interaction operator is: $\hat{V}_{{e-\gamma}}$
=$\frac{e}{2m^{\star}}(\hat{\vec{p}}.\vec{A}+\vec{A}.\hat{\vec{p}})$
Here, $\hat{\vec{p}}$ is the momentum operator, and $\vec{A}$ is the incident
photons’ vector potential.

Thus, the light-matter interaction matrix can be written as Aeberhard (2012);
Bertazzi et al. (2020); Kolek (2019).

$[\tau^{\beta}]=\frac{e\hbar}{2}[(\frac{\vec{k}_{hole}}{m^{\star}_{h}}+\frac{\vec{k}_{electron}}{m^{\star}_{e}}).\vec{A}_{O}]b^{ph}_{\beta}$
(15)

The gate (reservoir) Green’s function $(G^{G}_{ks}(t,t^{{}^{\prime}}))$ leads
to

$c_{k}(t)=c^{iso}_{k}(t)+\sum_{i}\int{dt^{{}^{\prime}}}{\sum_{s}G^{G}_{ks}(t,t^{{}^{\prime}})\zeta_{si}^{\star}c_{i}(t^{{}^{\prime}})}$
(16)

which, putting into 5a, gives rise to,

$i\hbar\frac{d}{dt}c_{i}=H^{c}_{iso}c_{i}+\sum_{j}\int{dt^{{}^{\prime}}}{\sum_{G}(t.t^{{}^{\prime}})_{ij^{{}^{\prime}}}c_{j^{{}^{\prime}}}(t^{{}^{\prime}})}+(S_{G}(t))_{i}+\sum_{j,\alpha}(\tau^{\beta}_{ij}v_{j}b_{\beta}+\tau^{\beta\star}_{ij}v_{j}b^{{\dagger}}_{\beta})$
(17)

Where $S_{G}(t)_{i}=\sum_{k}\zeta_{ik}c^{iso}_{k}(t)$ and
$\sum_{G}(t,t^{{}^{\prime}})_{ij^{{}^{\prime}}}=\sum_{k,s}\zeta_{lk}G^{G}_{ks}(t,t^{{}^{\prime}})\zeta^{\star}_{sj^{{}^{\prime}}}$

Figure  6: Interaction picture used in our model and the flow of the NEGF
program. (a) The conventional picture of electron-hole pair generation due to
light (b) Feynman diagram corresponding to equivalent interaction picture
utilized for theoretical modeling. (c) Total theoretical modeling and
Schrodinger-Poisson’s self-consistency for quantum mechanically modulated
semi-classical diffusive transport.

The last term of equation-17 is calculated by starting from,

$\begin{split}i\hbar\frac{d}{dt}(v_{j}b_{\beta})&=\biggl{[}i\hbar\frac{d}{dt}v_{j}\biggr{]}b_{\beta}+v_{j}\biggl{[}i\hbar\frac{d}{dt}b_{\beta}\biggr{]}\\\
&=\biggl{[}[H^{V}_{\text{iso}}+E_{\beta}](v_{j}b_{\beta})+\sum_{l}\int
dt^{\prime}\biggl{(}\sum_{R}(t,t^{\prime})\biggr{)}_{jk}c_{k^{\prime}}(t^{\prime})b_{\beta}\biggr{]}\\\
&\quad+\biggl{[}\sum_{j^{\prime},\beta^{\prime}}(\tau^{\beta^{\prime}}_{ij^{\prime}}{}^{\star}c_{i}b_{\beta^{\prime}})+\sum_{i,j^{\prime}}(\tau^{\beta^{\prime}}_{jj^{\prime}}{}^{\star}c_{j^{\prime}}v_{r}v_{j^{\prime}}^{{\dagger}})\biggr{]}.\end{split}$
(18)

The last two terms can be again simplified to reachSikdar et al. (2017),

$\biggl{[}i\hbar\frac{d}{dt}-[H^{v}_{iso}+E_{\beta}]\biggr{]}(v_{j}b_{\beta})=\Xi_{j\beta}$
(19)

where
$\Xi_{j\beta}=\sum_{j^{{}^{\prime}}}\biggl{[}{\tau^{\beta^{{}^{\prime}}}_{j^{{}^{\prime}}s}}^{\star}\biggl{(}[b^{{\dagger}}_{\beta}b_{\beta}][v^{{\dagger}}_{s}v_{j}]+[b_{\beta}b^{{\dagger}}_{\beta^{{}^{\prime}}}][v^{{\dagger}}_{j}v_{s}]\biggr{)}c_{j^{{}^{\prime}}}\biggr{]}$

Equation-19 can be solved by calculating the electron-photon Green’s function
as follows.

$\begin{split}[v_{j}b_{\beta}]&=[v_{j}b_{\beta}]^{iso}+\int
dt^{{}^{\prime}}G^{e-\gamma}_{j\beta}(t,t^{{}^{\prime}})\Xi_{j\beta}(t^{{}^{\prime}})\\\
&=[v_{j}b_{\beta}]^{iso}+\int
dt^{{}^{\prime}}\Theta(t-t^{{}^{\prime}})\sum_{j^{{}^{\prime}}}\biggl{[}\tau^{\beta^{{}^{\prime}}}_{j^{{}^{\prime}}s^{\star}}[N^{ab-
pht}_{\beta\beta^{{}^{\prime}}}(t,t^{{}^{\prime}})][n^{v}_{sj}(t,t^{{}^{\prime}})]\\\
&\quad+[N^{em-
pht}_{\beta\beta^{{}^{\prime}}}(t,t^{{}^{\prime}})][p^{v}_{sj}(t,t^{{}^{\prime}})]c_{j^{{}^{\prime}}}\biggr{]}\end{split}$
(20)

Similarly $v_{j}{b_{\beta}}^{{\dagger}}$ can be written as,

$\begin{split}[v_{j}{b_{\beta}}^{{\dagger}}]&=[v_{j}{b_{\beta}}^{{\dagger}}]^{iso}+\int
dt^{{}^{\prime}}\Theta(t-t^{{}^{\prime}})\sum_{j^{{}^{\prime}}}\biggl{[}\tau^{\beta^{{}^{\prime}}}_{j^{{}^{\prime}}s^{\star}}[N^{em-
pht}_{\beta\beta^{{}^{\prime}}}(t,t^{{}^{\prime}})][n^{v}_{sj}(t,t^{{}^{\prime}})]\\\
&\quad+[N^{ab-
pht}_{\beta\beta^{{}^{\prime}}}(t,t^{{}^{\prime}})][p^{v}_{sj}(t,t^{{}^{\prime}})]c_{j^{{}^{\prime}}}\biggr{]}\end{split}$
(21)

substituting the equation-20 and 21 in the equation-5, we get,

$\displaystyle i\hbar\frac{d}{dt}c_{i}$
$\displaystyle=H^{c}_{iso}c_{i}+\sum_{j,\beta}(\tau^{\beta}_{ij}[v_{j}b_{\beta}]^{iso}+{\tau^{\beta}_{ij}}^{\star}[v_{j}{b_{\beta}}^{{\dagger}}]^{iso})$
$\displaystyle\quad+\sum_{r}\xi_{ik}c_{k}+\sum_{j^{{}^{\prime}}}\int{dt^{{}^{\prime}}}(\Sigma_{\text{elec}}(t,t^{{}^{\prime}})_{rs}c_{j^{{}^{\prime}}})$
(22)

where

$[\Sigma_{\text{elec}}(t,t^{{}^{\prime}})]_{rs}=\sum_{(l,k,\beta,\beta^{{}^{\prime}})}\left\\{\tau^{\beta}_{rl}\left[N^{\text{ab}\text{-}\text{pht}}_{\beta\beta^{{}^{\prime}}}(t,t^{{}^{\prime}})n^{v}_{lk}(t,t^{{}^{\prime}})\right]^{\text{iso}}(\tau^{\beta^{{}^{\prime}}}_{ks})^{\star}+(\tau^{\beta}_{rl})^{\star}\left[N^{\text{em}\text{-}\text{pht}}_{\beta\beta^{{}^{\prime}}}(t,t^{{}^{\prime}})n^{v}_{lk}(t,t^{{}^{\prime}})\right]^{\text{iso}}(\tau^{\beta^{{}^{\prime}}}_{ks})^{\star}\right\\}$
(23)

In our device, the possibility of carrier recombination is negligible due to
voltage-dependent separation. Hence, the second term of the R.H.S of
equation-23 can be neglected. So, we find,

$[\Sigma_{\text{elec}}(t,t^{{}^{\prime}})]_{rs}=\sum_{(l,k,\beta,\beta^{{}^{\prime}})}\left\\{\tau^{\beta}_{rl}\left[N^{ab\text{-}pht}_{\beta\beta^{{}^{\prime}}}(t,t^{{}^{\prime}})n^{v}_{lk}(t,t^{{}^{\prime}})\right]^{\text{iso}}\tau^{\beta^{\prime}}_{ks}\right\\}$
(24)

These entire calculations can be similarly followed for holes, and so reach
to,

$[\Sigma_{\text{hole}}(t,t^{{}^{\prime}})]_{pq}=\sum_{(l^{\prime},k^{\prime},\beta,\beta^{{}^{\prime}})}\left\\{\tau^{\beta}_{pl^{\prime}}\left[N^{ab\text{-}pht}_{\beta\beta^{{}^{\prime}}}(t,t^{{}^{\prime}})n^{c}_{l^{\prime},k^{\prime}}(t,t^{{}^{\prime}})\right]^{\text{iso}}\tau^{\beta^{\prime}}_{k^{\prime}q}\right\\}$
(25)

Where the number of absorbed photons is given as:
$N^{ab\text{-}pht}_{\beta\beta^{\prime}}=\frac{\biggl{\\{}\left(\frac{I_{\beta}}{E_{\beta}}\right)V_{ab}\delta_{\beta\beta^{\prime}}\biggr{\\}}}{\tilde{v}}$

$I_{\beta}$, $n^{c}$, $n^{v}$, and $V_{ab}$ are the intensity of the laser
source, correlation function of the filled states in the conduction band,
valence band, and the absorbing volume, respectively. At the same time, $\tau$
represents the interaction potential governing the device’s photo generation
(electron-hole pair). The velocity of light within the material is
$\tilde{v}=\frac{c}{n^{\lambda}}$, where $n^{\lambda}$ is the refractive index
of Si for the corresponding wavelength of the laser source.

Device Fabrication : Figure-1a shows the schematic of a tri-gated nanowire
junctionless transistor. The nanowire is highly doped ($\sim 3\times
10^{19}\,\mathrm{cm}^{-3}$) such that we can have a high amount of ON current.
The gate material surrounded the nanowire from three directions, as shown in
figure-1, to increase the gate control over the channel, and this tri-gate has
the same functionality as a conventional single gate. Therefore, this tri-gate
geometry can easily control the flow of carriers from source to drain with the
highest precision. We use $n$-type nanowire or channel and p-type gate in this
work. Therefore, we now put a $p$-type gate on the $n$-type channel; due to
work function difference and very low nanowire thickness (5 nm), the channel
beneath the gate got depleted completely. So, this type of junction-less
device works in accumulation mode, which is unlikely like other devices with
junctions. To fabricate tri-gated silicon nanowire-based junctionless MOSFET,
we used silicon-on-insulator (SOI) wafers with a few high-quality nanometers
of top silicon. We did ion implantation of arsenic to dope the top silicon of
SOI wafer into n-type silicon, and resultant arsenic doping was in the order
of $3\times 10^{19}$ $cm^{-3}$. After making the top silicon into n-type, we
did nanolithography using electron beam lithography (EBL) to define the
nanowire, where we spin-coated top silicon with negative electron resist
Hydrogen Silsesquioxane (HSQ). Next, we did reactive ion etching (RIE) of EBL
patterned nanowire, and finally, silicon nanowire (Si NW) was realized. After
realizing the nanowire, we did dry oxidation of Sio2 on the top of the
nanowire to get 10 nm top gate oxide. Then, we patterned the oxide over the
nanowire. In the next step to realizing 1 um top gate, we deposited 50 nm
amorphous silicon in a low-pressure chemical vapor chamber at $550^{\circ}c$.
We doped the amorphous silicon heavily with boron to make $P^{+}$ top gate.
After this, we annealed the sample for 30 mins at $900^{\circ}c$ in nitrogen
to make it polycrystalline silicon from amorphous silicon. Then, we did EBL
and RIE to pattern the polysilicon to define the 1-um gate. To isolate the
gate from source and drain contact, we deposited a very slim layer of sio2. To
make source and drain contact, we opened a window in the sio2 via
photolithography and deposited Ti/W-Al. The scanning electron microscopy (SEM)
image of fabricated tri-gated silicon nanowire MOSFET is shown in
figure-1b.The whole fabrication steps are illustrated in SN-5 of SI NAT
(2024).

Photo Response Measurement: The photo responsiveness of the Tri-Gated
Junctionless (JL) nanowire n-channel transistor we fabricated was assessed
under light exposure with a wavelength of 700 nm. Illuminating our Nanowire
MOSFET with a laser beam with incident power on the nanowire is 4.12 nW. This
illumination induces electron excitation, resulting in the production of a
measurable current. Subsequently, the source measuring unit captures and
examines this current, offering valuable information about NW MOSFET’s
photoconductivity and electronic characteristics. In Figure 2, the transfer
characteristics of our fabricated device are depicted under constant
illumination conditions while maintaining a fixed drain-to-source voltage of
50 mV. Even at incident power on the nanowire is 4.12 nW, our device exhibited
a discernible photo response compared to the non-illuminated case,
highlighting its high sensitivity. To further understand the photo response,
we varied the drain-to-source voltage from -200 mV to +200 mV, maintaining the
same illumination conditions ( laser beam with incident power on the nanowire
is 4.12 nW and a wavelength of 700 nm).

##  VI Data Avaibility

The datasets produced in this research can be acquired by making a reasonable
request to the corresponding author.

##  VII Acknowledgement

The authors thank the Defence Research and Development Organisation (DRDO) and
the Department of Science and Technology (DST) for financial support. BK
acknowledges the financial assistance from the Prime Minister Research Fellows
(PMRF) Scheme (PMRF ID: 1401633), India. Additionally, special appreciation is
expressed to James Haigh from Hitachi Cambridge Laboratory for assisting with
optical measurements.

##  VIII Author Contributions

BK and AM have made equal contributions to this article.

##  IX Competing interests

The authors declare no competing interests.

Correspondence and inquiries for materials should be directed to S.D.

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